Structural Dynamics of Rhodopsins using Time-Resolved X ...

Thesis for the degree of Doctor of Philosophyin the Natural Sciences

Structural Dynamics ofRhodopsins using

Time-Resolved X-ray SolutionScattering

Daniel Sarabi

Department of Chemistry and Molecular Biology

Gothenburg, 2022

Structural Dynamics of Rhodopsins using Time-Resolved X-ray Solu-tion Scattering

Daniel Sarabi

Cover: Bacteriorhodopsin within a detergent micelle consisting of 190molecules of β-octyl glucoside

Department of Chemistry and Molecular BiologyDivision of Biochemistry and Structural BiologyUniversity of GothenburgSE-405 30, Göteborg, SwedenPrinted by StemaGöteborg, Sweden, 2022

Gothenburg, 2022

Structural Dynamics of Rhodopsins using Time-Resolved X-ray Solu-tion Scattering

Daniel Sarabi

Cover: Bacteriorhodopsin within a detergent micelle consisting of 190molecules of β-octyl glucoside

Department of Chemistry and Molecular BiologyDivision of Biochemistry and Structural BiologyUniversity of GothenburgSE-405 30, Göteborg, SwedenPrinted by StemaGöteborg, Sweden, 2022

SVANENMÄRKET

Trycksak3041 0234

AbstractLight is one of the most important sources of energy and environ-

mental signals, and many organisms adapt in response to the presenceof light. This is made possible through specialised proteins called pho-toreceptors. Rhodopsins are made light-sensitive via the addition ofa retinal chromophore, and activated by specific wavelengths of light,which propagates a cascade of structural changes, allowing the proteinto carry out it’s function. Microbial rhodopsins have been found toact as light-driven proton pumps, light-gated ion channels or recep-tors for phototaxis, whereas rhodopsin in higher eukaryotes such asthe human eye, is responsible for low-light vision.

Time-resolved X-ray solution scattering (TR-XSS) is a sub-fieldof structural biology which can detect secondary structural changesin proteins as they evolve along their functional pathways in realtime. The method addresses many of the limitations of serial crys-tallography, however, structural modelling is more challenging due tothe measured information being one-dimensional. Further challengesarise in structural interpretation of TR-XSS data recorded from in-tegral membrane proteins, due to the presence of a detergent micellesurrounding the protein. In our previous modelling, the interferencebetween the protein and micelle was not addressed, and in this the-sis we utilize molecular dynamics simulations to explicitly incorpo-rate the X-ray scattering cross-term between an integral membraneprotein and it’s surrounding micelle when modelling against TR-XSSdata from photo-activated rhodopsins within a detergent micelle. Theinfluence of the detergent micelle and micelle size on difference X-rayscattering was determined, correction for the solvent excluded volumewas made and candidate motions were identified using these proto-cols for modelling against TR-XSS data of bacteriorhodopsin, SRIIin isolation and in complex with it’s transducer protein HrII, andChannelrhodopsin 2. The analysis tools and protocols developed inthis thesis should provide a framework for the analysis and structural

AbstractLight is one of the most important sources of energy and environ-

mental signals, and many organisms adapt in response to the presenceof light. This is made possible through specialised proteins called pho-toreceptors. Rhodopsins are made light-sensitive via the addition ofa retinal chromophore, and activated by specific wavelengths of light,which propagates a cascade of structural changes, allowing the proteinto carry out it’s function. Microbial rhodopsins have been found toact as light-driven proton pumps, light-gated ion channels or recep-tors for phototaxis, whereas rhodopsin in higher eukaryotes such asthe human eye, is responsible for low-light vision.

Time-resolved X-ray solution scattering (TR-XSS) is a sub-fieldof structural biology which can detect secondary structural changesin proteins as they evolve along their functional pathways in realtime. The method addresses many of the limitations of serial crys-tallography, however, structural modelling is more challenging due tothe measured information being one-dimensional. Further challengesarise in structural interpretation of TR-XSS data recorded from in-tegral membrane proteins, due to the presence of a detergent micellesurrounding the protein. In our previous modelling, the interferencebetween the protein and micelle was not addressed, and in this the-sis we utilize molecular dynamics simulations to explicitly incorpo-rate the X-ray scattering cross-term between an integral membraneprotein and it’s surrounding micelle when modelling against TR-XSSdata from photo-activated rhodopsins within a detergent micelle. Theinfluence of the detergent micelle and micelle size on difference X-rayscattering was determined, correction for the solvent excluded volumewas made and candidate motions were identified using these proto-cols for modelling against TR-XSS data of bacteriorhodopsin, SRIIin isolation and in complex with it’s transducer protein HrII, andChannelrhodopsin 2. The analysis tools and protocols developed inthis thesis should provide a framework for the analysis and structural

modelling of light-activated integral membrane proteins as a powerfulcomplement to other biophysical methods.

AcknowledgementsThe path towards a PhD is indeed an enormous team-effort. Be-

ing an independent researcher is a highly praised quality, yet withoutproper mentorship, support from colleagues, friends and family, itmight be an impossible achievement. So therefore I want to start bythanking my supervisor Richard Neutze for giving me this oppor-tunity to partake in a great scientific journey, that took me too manyplaces around the world, creating memories for life. Thank you forthe mentorship, the hours and hours on zoom, the million emails andsleepless nights working with me towards this thesis. When you in thefirst week of my PhD put me in charge of data analysis at ID09 inGrenoble, and brought two oversea students back to Gothenburg forme to teach them analysis of TR-XSS data, I knew that you had highhopes for me. I spent half a day trying to install MATLAB on a linuxcomputer. Thanks for the trust, it motivated me to evolve!

The countless nights spent in a underground bunker at manybeamtimes would not have been as fun and memorable without mywonderful colleagues. Robert Bosman, a crazy, fun, sporadic, intel-ligent, dark-humoured, kind person that hates small-talk, have alsobeen central to my PhD. The number of strange texts messages thatyou have sent me would surely grant me numerous restraining orders,yet our friendship persisted. We have been on many experiments to-gether, writing code, spent late nights on the computer cluster de-bugging a script, socialized, and I was even invited to your wedding.A friend indeed. Lucija Ostojić, you have also been central to meactually finishing my PhD, the number of simulations that you haveran for this project would power a small city in Croatia I’m sure.Thank you for the laughs the tears, the support, the three hour zoommeetings, the pdb files edited, the hundreds of retinal put into pro-teins, the problems you solved, I appreciate you! Thank you OskarBerntsson for passing the solution scattering flag before leaving, andfor answering the hundreds of questions I had and helping out withmy journey into MATLAB. And also thank you for coming all the

modelling of light-activated integral membrane proteins as a powerfulcomplement to other biophysical methods.

AcknowledgementsThe path towards a PhD is indeed an enormous team-effort. Be-

ing an independent researcher is a highly praised quality, yet withoutproper mentorship, support from colleagues, friends and family, itmight be an impossible achievement. So therefore I want to start bythanking my supervisor Richard Neutze for giving me this oppor-tunity to partake in a great scientific journey, that took me too manyplaces around the world, creating memories for life. Thank you forthe mentorship, the hours and hours on zoom, the million emails andsleepless nights working with me towards this thesis. When you in thefirst week of my PhD put me in charge of data analysis at ID09 inGrenoble, and brought two oversea students back to Gothenburg forme to teach them analysis of TR-XSS data, I knew that you had highhopes for me. I spent half a day trying to install MATLAB on a linuxcomputer. Thanks for the trust, it motivated me to evolve!

The countless nights spent in a underground bunker at manybeamtimes would not have been as fun and memorable without mywonderful colleagues. Robert Bosman, a crazy, fun, sporadic, intel-ligent, dark-humoured, kind person that hates small-talk, have alsobeen central to my PhD. The number of strange texts messages thatyou have sent me would surely grant me numerous restraining orders,yet our friendship persisted. We have been on many experiments to-gether, writing code, spent late nights on the computer cluster de-bugging a script, socialized, and I was even invited to your wedding.A friend indeed. Lucija Ostojić, you have also been central to meactually finishing my PhD, the number of simulations that you haveran for this project would power a small city in Croatia I’m sure.Thank you for the laughs the tears, the support, the three hour zoommeetings, the pdb files edited, the hundreds of retinal put into pro-teins, the problems you solved, I appreciate you! Thank you OskarBerntsson for passing the solution scattering flag before leaving, andfor answering the hundreds of questions I had and helping out withmy journey into MATLAB. And also thank you for coming all the

way to Grenoble to help me set up the first experiment, and show-ing me the first commands in shell. Thank you Giorgia for all thememories and laughs, for always boosting my confidence and for re-defining my understanding of what a pizza is! Thank you Gregerfor all the good times together, the friendship and for making mean "integral" part of the microtubules project, I quote: "We need aable bodied young man to help out". Thank you Per for allowingme to say Per in a British accent every time I’ve seen you for thepast 4 years, and also for helping out at the experiments. Thank youAdams for the support, and for writing that essential bash scriptwe needed. We still use it! Thank you Petra Båth for helping outwith making beautiful PyMOL figures. I have never seen a 164 linelong PyMOL script before. I don’t think anyone has ever asked me:"...Would you like the bR figure to look like sausages? It seems to bepretty fashionable these days". Thank you Cecilia Wickstrand forthe bR knowledge and your insightful reviews that saved me hoursand hours of literature searches, and for contributing to the spherescript! Thank you Swagatha question! Ghosh, Rob Dods, CeciliaSafari Rebecka Andersson. The Burmann group members: Thankyou Ylber Sallova, you are a champ! Emelie, Darius,Jens Lid-man and DAMASUS!. Thank you Amke Nimmrich, Ann Ooi,Weixiao, Andreas Dunge, Andrea Cellini, Laras aka smoothface! Doris Zorić, Leocadie Henry,Elin Claesson, Tinna Björg,Maja Jensen (technically danish), Maria José, Matthjis Pan-man, Rajiv Harimoorthy, Viktor Ahlberg, Florian Schmitz,Gisela Brändén,Gergely Katona, Sebastian Westenhoff, MichalMaj, Anna Reymer, Leif Eriksson, Magnus Andersson, DavidVan der Spoel. I want to thank Matteo Levantino and MichaelWulff at ESRF-ID09 for the support and knowledge about time-resolved X-ray scattering experiments. It has been an absolute plea-sure to have been surrounded by all of these wonderful people. I willalways carry these memories with me. I want to thank my close friends,my parents and my own family. Thank you Tinna and my daughterMitra Ìsold for all the support and love throughout this. It would nothave been possible without you two. I love you both endlessly!

PublicationsThis thesis consists of the following research papers:

PAPER I: Sarabi,D., Ostojić,L., Bosman,R., Vallejos,A., Linse,J.,Wulff,M., Levantino,M., Neutze,R. "Modelling differ-ence X-ray scattering observations from an integralmembrane protein within a detergent micelle." Sub-mitted.

PAPER II: Sarabi,D., Bosman,R., Ostojić,L., Ghosh,S., Ortolani,G.,Levantino,M., Pedersen,M., Sander,M., Båth,P., Dods,R.,Hammarin,G., Safari,C., Wulff,M., Brändén,G., Neutze,R."Time resolved X-ray solution scattering observationsof light-induced structural changes in Sensory RhodopsinII." Manuscript.

PAPER III: Sarabi,D., Ortolani,G., Wickstrand,C., Ostojić,L., Ghosh,S.,Bosman,R., Pedersen,M., Sander,M., Båth,P., Dods,R.,Hammarin,G., Safari,C., Levantino,M., Wulff,M., Brändén,G.,Neutze,R. "Time-resolved X-ray solution scatteringstudies of structural changes in Channelrhodopsin 2."Manuscript.

way to Grenoble to help me set up the first experiment, and show-ing me the first commands in shell. Thank you Giorgia for all thememories and laughs, for always boosting my confidence and for re-defining my understanding of what a pizza is! Thank you Gregerfor all the good times together, the friendship and for making mean "integral" part of the microtubules project, I quote: "We need aable bodied young man to help out". Thank you Per for allowingme to say Per in a British accent every time I’ve seen you for thepast 4 years, and also for helping out at the experiments. Thank youAdams for the support, and for writing that essential bash scriptwe needed. We still use it! Thank you Petra Båth for helping outwith making beautiful PyMOL figures. I have never seen a 164 linelong PyMOL script before. I don’t think anyone has ever asked me:"...Would you like the bR figure to look like sausages? It seems to bepretty fashionable these days". Thank you Cecilia Wickstrand forthe bR knowledge and your insightful reviews that saved me hoursand hours of literature searches, and for contributing to the spherescript! Thank you Swagatha question! Ghosh, Rob Dods, CeciliaSafari Rebecka Andersson. The Burmann group members: Thankyou Ylber Sallova, you are a champ! Emelie, Darius,Jens Lid-man and DAMASUS!. Thank you Amke Nimmrich, Ann Ooi,Weixiao, Andreas Dunge, Andrea Cellini, Laras aka smoothface! Doris Zorić, Leocadie Henry,Elin Claesson, Tinna Björg,Maja Jensen (technically danish), Maria José, Matthjis Pan-man, Rajiv Harimoorthy, Viktor Ahlberg, Florian Schmitz,Gisela Brändén,Gergely Katona, Sebastian Westenhoff, MichalMaj, Anna Reymer, Leif Eriksson, Magnus Andersson, DavidVan der Spoel. I want to thank Matteo Levantino and MichaelWulff at ESRF-ID09 for the support and knowledge about time-resolved X-ray scattering experiments. It has been an absolute plea-sure to have been surrounded by all of these wonderful people. I willalways carry these memories with me. I want to thank my close friends,my parents and my own family. Thank you Tinna and my daughterMitra Ìsold for all the support and love throughout this. It would nothave been possible without you two. I love you both endlessly!

PublicationsThis thesis consists of the following research papers:

PAPER I: Sarabi,D., Ostojić,L., Bosman,R., Vallejos,A., Linse,J.,Wulff,M., Levantino,M., Neutze,R. "Modelling differ-ence X-ray scattering observations from an integralmembrane protein within a detergent micelle." Sub-mitted.

PAPER II: Sarabi,D., Bosman,R., Ostojić,L., Ghosh,S., Ortolani,G.,Levantino,M., Pedersen,M., Sander,M., Båth,P., Dods,R.,Hammarin,G., Safari,C., Wulff,M., Brändén,G., Neutze,R."Time resolved X-ray solution scattering observationsof light-induced structural changes in Sensory RhodopsinII." Manuscript.

PAPER III: Sarabi,D., Ortolani,G., Wickstrand,C., Ostojić,L., Ghosh,S.,Bosman,R., Pedersen,M., Sander,M., Båth,P., Dods,R.,Hammarin,G., Safari,C., Levantino,M., Wulff,M., Brändén,G.,Neutze,R. "Time-resolved X-ray solution scatteringstudies of structural changes in Channelrhodopsin 2."Manuscript.

Related papers that I have co-authored but that are not included inthis thesis:

PAPER VI: Gruhl,T., Weinert,T., Rodrigues,M., Milne,C., Or-tolani, G., Nass,K., Nango,E., Sen,Saumik., John-son,P., Cirelli,C., Furrer,A., Mous, S., Skopintsev,P.,James,D., Dworkowski,F., Båth,P., Kekilli,D., Oze-rov,D., Tanaka,R., Glover,H., Bacellar,C.,Brühnle,S.,Casadei,C., Diethelm,A., Gashi,D., Gotthard,G., Joti,Y.,Knopp,G., Lesca,E., Ma,P., Martiel,I., Mühle,J., Owada,S.,Pamula,F., Sarabi,D., Tejero,O., Tsai,CJ., Varma,N.,Wach,A., Boutet,S., Tono,K., Nogly,P., Deupi,X., Iwata,S.,Neutze,R., Standfuss,J., Schertler,G., Panneels,V. "Ul-trafast structural changes direct the first molecularevents of vision." Submitted.

Contribution report

PAPER I: I designed the research together with my supervisor.I took part in execution of the X-ray solution scatter-ing experiment, in addition to collection and analysisof data. I played a major part of constructing proto-cols towards structural modelling of the data. I madecontributions to the manuscript.

PAPER II: I took part in the execution of the X-ray solutionscattering experiment, in addition to collection andanalysis of data. I took major part in the structuralanalysis of the data and made contributions to themanuscript.

PAPER III: I took part in the execution of the X-ray solutionscattering experiment, in addition to collection andanalysis of data. I took major part in the structuralanalysis of the data and made contributions to themanuscript.

Related papers that I have co-authored but that are not included inthis thesis:

PAPER VI: Gruhl,T., Weinert,T., Rodrigues,M., Milne,C., Or-tolani, G., Nass,K., Nango,E., Sen,Saumik., John-son,P., Cirelli,C., Furrer,A., Mous, S., Skopintsev,P.,James,D., Dworkowski,F., Båth,P., Kekilli,D., Oze-rov,D., Tanaka,R., Glover,H., Bacellar,C.,Brühnle,S.,Casadei,C., Diethelm,A., Gashi,D., Gotthard,G., Joti,Y.,Knopp,G., Lesca,E., Ma,P., Martiel,I., Mühle,J., Owada,S.,Pamula,F., Sarabi,D., Tejero,O., Tsai,CJ., Varma,N.,Wach,A., Boutet,S., Tono,K., Nogly,P., Deupi,X., Iwata,S.,Neutze,R., Standfuss,J., Schertler,G., Panneels,V. "Ul-trafast structural changes direct the first molecularevents of vision." Submitted.

Contribution report

PAPER I: I designed the research together with my supervisor.I took part in execution of the X-ray solution scatter-ing experiment, in addition to collection and analysisof data. I played a major part of constructing proto-cols towards structural modelling of the data. I madecontributions to the manuscript.

PAPER II: I took part in the execution of the X-ray solutionscattering experiment, in addition to collection andanalysis of data. I took major part in the structuralanalysis of the data and made contributions to themanuscript.

PAPER III: I took part in the execution of the X-ray solutionscattering experiment, in addition to collection andanalysis of data. I took major part in the structuralanalysis of the data and made contributions to themanuscript.

AbbreviationsHere follows a list and short explanation of the different abbreviationsused in this thesis.

BOG β-octylglucosidebR BacterioRhodopsinChR2 ChannelRhodopsin-2DDM n-Dodecyl-β-D-maltosideESRF European Synchrotron Raditation Facilityfs Femtosecond (10−15s)FWHM Full Width Half MaximumGeV Giga-electron Volt (109eV )GPCR G-protein coupled receptorHOMO Highest Occupied Molecular OrbitalMD Molecular DynamicsID09b Insertion Device 09b (beamline at the ESRF)LUMO Lowest Occupied Molecular Orbitalµs Microsecond (10−5s)ms Milliosecond (10−3s)ns Nanosecond (10−9s)ps Picosecond (10−12)rho Visual rhodopsinRMSD Root Mean Square DeviationSAXS Small Angle X-ray ScatteringSLS Swiss Light SourceSRII Sensory rhodopsin IISVD Singular Value DecompositionTR-XSS Time-Resolved X-ray Solution ScatteringWAXS Wide Angle X-ray ScatteringÅ Ångström (10−10m)

AbbreviationsHere follows a list and short explanation of the different abbreviationsused in this thesis.

BOG β-octylglucosidebR BacterioRhodopsinChR2 ChannelRhodopsin-2DDM n-Dodecyl-β-D-maltosideESRF European Synchrotron Raditation Facilityfs Femtosecond (10−15s)FWHM Full Width Half MaximumGeV Giga-electron Volt (109eV )GPCR G-protein coupled receptorHOMO Highest Occupied Molecular OrbitalMD Molecular DynamicsID09b Insertion Device 09b (beamline at the ESRF)LUMO Lowest Occupied Molecular Orbitalµs Microsecond (10−5s)ms Milliosecond (10−3s)ns Nanosecond (10−9s)ps Picosecond (10−12)rho Visual rhodopsinRMSD Root Mean Square DeviationSAXS Small Angle X-ray ScatteringSLS Swiss Light SourceSRII Sensory rhodopsin IISVD Singular Value DecompositionTR-XSS Time-Resolved X-ray Solution ScatteringWAXS Wide Angle X-ray ScatteringÅ Ångström (10−10m)

Contents

Acknowledgements vii

Abbreviations xiii

1 Introduction 11.1 The Biological Membrane . . . . . . . . . . . . . . . . 11.2 Photoreceptor & Retinylidine Proteins . . . . . . . . . 41.3 Scope of this thesis . . . . . . . . . . . . . . . . . . . . 7

2 Microbial and animal rhodopsins 92.1 Bacteriorhodopsin . . . . . . . . . . . . . . . . . . . . 92.2 Sensory rhodopsin II and transducer . . . . . . . . . . 132.3 Channelrhodopsin . . . . . . . . . . . . . . . . . . . . 172.4 Visual rhodopsin . . . . . . . . . . . . . . . . . . . . . 19

3 Protein dynamics 233.1 Time resolved structural biology . . . . . . . . . . . . 23

4 X-ray scattering 334.1 Fundamentals . . . . . . . . . . . . . . . . . . . . . . . 34

5 Time-resolved X-ray solution scattering methodology 415.1 Generating intense X-rays-synchrotron light source . . 415.2 Experimental setup . . . . . . . . . . . . . . . . . . . . 425.3 Data processing . . . . . . . . . . . . . . . . . . . . . . 455.4 Structural analysis and modeling . . . . . . . . . . . . 50

Contents

Acknowledgements vii

Abbreviations xiii

1 Introduction 11.1 The Biological Membrane . . . . . . . . . . . . . . . . 11.2 Photoreceptor & Retinylidine Proteins . . . . . . . . . 41.3 Scope of this thesis . . . . . . . . . . . . . . . . . . . . 7

2 Microbial and animal rhodopsins 92.1 Bacteriorhodopsin . . . . . . . . . . . . . . . . . . . . 92.2 Sensory rhodopsin II and transducer . . . . . . . . . . 132.3 Channelrhodopsin . . . . . . . . . . . . . . . . . . . . 172.4 Visual rhodopsin . . . . . . . . . . . . . . . . . . . . . 19

3 Protein dynamics 233.1 Time resolved structural biology . . . . . . . . . . . . 23

4 X-ray scattering 334.1 Fundamentals . . . . . . . . . . . . . . . . . . . . . . . 34

5 Time-resolved X-ray solution scattering methodology 415.1 Generating intense X-rays-synchrotron light source . . 415.2 Experimental setup . . . . . . . . . . . . . . . . . . . . 425.3 Data processing . . . . . . . . . . . . . . . . . . . . . . 455.4 Structural analysis and modeling . . . . . . . . . . . . 50

6 Results and Discussion 576.1 Modelling difference X-ray scattering observations from

an integral membrane protein within a detergent mi-celle (PAPER I) . . . . . . . . . . . . . . . . . . . . . 57

6.2 Time-resolved X-ray Solution Scattering Observationsof Light-Induced Structural Changes in Sensory RhodopsinII (PAPER II) . . . . . . . . . . . . . . . . . . . . . . 74

6.3 Time-resolved X-ray Solution Scattering Studies of Struc-tural Changes in Channelrhodopsin 2 (PAPER III) . . 83

7 Concluding remarks and future outlook 89

Bibliography 92

For Mitra Ìsold & Tinna. . .

6 Results and Discussion 576.1 Modelling difference X-ray scattering observations from

an integral membrane protein within a detergent mi-celle (PAPER I) . . . . . . . . . . . . . . . . . . . . . 57

6.2 Time-resolved X-ray Solution Scattering Observationsof Light-Induced Structural Changes in Sensory RhodopsinII (PAPER II) . . . . . . . . . . . . . . . . . . . . . . 74

6.3 Time-resolved X-ray Solution Scattering Studies of Struc-tural Changes in Channelrhodopsin 2 (PAPER III) . . 83

7 Concluding remarks and future outlook 89

Bibliography 92

For Mitra Ìsold & Tinna. . .

Chapter 1

Introduction

1.1 The Biological MembraneThe English physicist Robert Hook (1635-1702) published a paper in1665 in the journal Micrographia where he described the microscopicalunits that made up a slice of cork and coined the term "cells", fromthe latin word cella meaning ’a small room’ [1]. The biological mem-brane is essential for life, it defines the cell and organisms, allowingcompartmentalization and logical barriers between ’inside’ and ’out-side’, separating the cell from the external environment or for creatingintracellular compartments. This allows selective regulation of whatmolecules are allowed in or out of a given compartment, and thus thepresence of the biomembrane creates unique chemical environmentsfor a wide variety of specialized functions [2]. Membranes can provideenergy to cells derived from chemical or charge gradients, regulate en-zymatic activity, facilitate signal transduction and provide substratesused for signalling molecules or biosynthesis [3].

Biological membranes, like the one that envelopes around the cell,consists of three main building blocks; lipids, membrane proteins,and carbohydrates. Phospholipid molecules are amphipathic as theyare composed of a polar head (hydrophilic) and two non-polar tails(hydrophobic). The hydrophilic head group consists of a phosphate-containing group attached to a glycerol molecule. The hydrophobictails, each containing either a saturated or an unsaturated fatty acid,are long hydrocarbon chains. These molecules form the basic bilayerframework of the membrane, where the lipid molecules are arranged in

Chapter 1

Introduction

1.1 The Biological MembraneThe English physicist Robert Hook (1635-1702) published a paper in1665 in the journal Micrographia where he described the microscopicalunits that made up a slice of cork and coined the term "cells", fromthe latin word cella meaning ’a small room’ [1]. The biological mem-brane is essential for life, it defines the cell and organisms, allowingcompartmentalization and logical barriers between ’inside’ and ’out-side’, separating the cell from the external environment or for creatingintracellular compartments. This allows selective regulation of whatmolecules are allowed in or out of a given compartment, and thus thepresence of the biomembrane creates unique chemical environmentsfor a wide variety of specialized functions [2]. Membranes can provideenergy to cells derived from chemical or charge gradients, regulate en-zymatic activity, facilitate signal transduction and provide substratesused for signalling molecules or biosynthesis [3].

Biological membranes, like the one that envelopes around the cell,consists of three main building blocks; lipids, membrane proteins,and carbohydrates. Phospholipid molecules are amphipathic as theyare composed of a polar head (hydrophilic) and two non-polar tails(hydrophobic). The hydrophilic head group consists of a phosphate-containing group attached to a glycerol molecule. The hydrophobictails, each containing either a saturated or an unsaturated fatty acid,are long hydrocarbon chains. These molecules form the basic bilayerframework of the membrane, where the lipid molecules are arranged in

Chapter 1. Introduction

Figure 1.1: Membrane protein in bilayer Left: Bacteriorhodpsin isdepicted in a bilayer, with lipid tails of the two leaflets pointing in to-wards the centre and head pointing towards the aqueous solvent. Built usingCHARMM-GUI [4] Right: The phosphatidylglycerol DPPG lipid moleculewhich exists in the biological membrane. The hydrophilic head consists of aphosphoglycerol group, and the hydrophobic tail is an alkyl chain of carbonswhich can be saturated or partly unsaturated. Both images were renderedusing PyMOL [5].

two leaflets with the hydrophilic head group forming the edges of themembrane and the hydrophobic tails of each layer is pointed towardsthe center, buried away from the aqueous environment (figure 1.1).This creates a permeability barrier between the polar aqueous envi-ronment of the inside and the outside the cell, preventing ions andother polar solutes to spontaneously traverse across the membrane.The same is true for liposomes and micelles, where the micelle is asphere with a hydrophobic core (figure 1.2), and the liposome is aspherical vesicle, capable of transporting hydrophilic molecules in itscore, and shielding it from the outside aqueous environment.

Membrane proteins constitute a crucial part of the biological mem-brane, and like all proteins, are constructed from linear chains of co-valent peptide bonds between amino acid residues, translated from asequence of mRNA encoded by the DNA of it’s respective gene. Theunique chemical nature of amino acid side chains allow these linearchains to fold into ordered secondary structures such as α-helices and

1.1. The Biological Membrane

Figure 1.2: bR-micelle complex Bacteriorhodpsin in complex with mi-celle using beta octyl glucoside (BOG) detergent. This detergent is a gly-coside with a saturated alkyl chain. An important detergent for the purifi-cation of membrane proteins. Image was rendered using PyMOL [5].

β-sheets, which together form complex tertiary and quaternary struc-tures. The global structure and properties of a membrane protein isdetermined by the amino acid sequence. Furthermore, membrane pro-teins subtle biochemistry is afforded by the properties of amino acidsto facilitate their functions.

There are three main types of membrane proteins: lipid anchoredproteins, that are held into the membrane by covalently attached fattyacids or lipids; periphereral membrane proteins, that are located onthe surface of the membrane, and transmembrane (TM) proteins sitinto the membrane itself, and traverse the membrane to access bothsides of it. Because the polar and non-polar nature of biomembranes,integral membrane proteins contain a hydrophobic core surroundedby the lipid tails and hydrophilic cytoplasmic and extracellular re-gions towards the polar heads of the lipids and the aqueous environ-ment [6,7]. Due to the multitude of functions provided by membraneproteins such as signalling, transport, energy transduction, knowingthe structure and function provides a rich source in biology and cen-tral in the development of new drug targets to improve human andanimal health [3].

Figure 1.1: Membrane protein in bilayer Left: Bacteriorhodpsin isdepicted in a bilayer, with lipid tails of the two leaflets pointing in to-wards the centre and head pointing towards the aqueous solvent. Built usingCHARMM-GUI [4] Right: The phosphatidylglycerol DPPG lipid moleculewhich exists in the biological membrane. The hydrophilic head consists of aphosphoglycerol group, and the hydrophobic tail is an alkyl chain of carbonswhich can be saturated or partly unsaturated. Both images were renderedusing PyMOL [5].

two leaflets with the hydrophilic head group forming the edges of themembrane and the hydrophobic tails of each layer is pointed towardsthe center, buried away from the aqueous environment (figure 1.1).This creates a permeability barrier between the polar aqueous envi-ronment of the inside and the outside the cell, preventing ions andother polar solutes to spontaneously traverse across the membrane.The same is true for liposomes and micelles, where the micelle is asphere with a hydrophobic core (figure 1.2), and the liposome is aspherical vesicle, capable of transporting hydrophilic molecules in itscore, and shielding it from the outside aqueous environment.

Membrane proteins constitute a crucial part of the biological mem-brane, and like all proteins, are constructed from linear chains of co-valent peptide bonds between amino acid residues, translated from asequence of mRNA encoded by the DNA of it’s respective gene. Theunique chemical nature of amino acid side chains allow these linearchains to fold into ordered secondary structures such as α-helices and

1.1. The Biological Membrane

Figure 1.2: bR-micelle complex Bacteriorhodpsin in complex with mi-celle using beta octyl glucoside (BOG) detergent. This detergent is a gly-coside with a saturated alkyl chain. An important detergent for the purifi-cation of membrane proteins. Image was rendered using PyMOL [5].

β-sheets, which together form complex tertiary and quaternary struc-tures. The global structure and properties of a membrane protein isdetermined by the amino acid sequence. Furthermore, membrane pro-teins subtle biochemistry is afforded by the properties of amino acidsto facilitate their functions.

There are three main types of membrane proteins: lipid anchoredproteins, that are held into the membrane by covalently attached fattyacids or lipids; periphereral membrane proteins, that are located onthe surface of the membrane, and transmembrane (TM) proteins sitinto the membrane itself, and traverse the membrane to access bothsides of it. Because the polar and non-polar nature of biomembranes,integral membrane proteins contain a hydrophobic core surroundedby the lipid tails and hydrophilic cytoplasmic and extracellular re-gions towards the polar heads of the lipids and the aqueous environ-ment [6,7]. Due to the multitude of functions provided by membraneproteins such as signalling, transport, energy transduction, knowingthe structure and function provides a rich source in biology and cen-tral in the development of new drug targets to improve human andanimal health [3].

1.2 Photoreceptor & Retinylidine ProteinsSensing and reacting to a wide range of environmental cues, is essentialto all life on earth. Light is one of the most important sources ofenergy and environmental signals, and many organisms need to adaptin response to the presence of light [8]. This is made possible throughspecialised proteins called photoreceptors.

Photochemically reactive proteins have been described in archaeal,prokaryotic and eukaryotic organisms, expanding across all domainsof life [9]. These proteins mediate light responses such as visual per-ception, phototropism, phototaxis, and the circadian rhythm. Manyphotoreceptor proteins use a chromophore, embedded in the core ofthe protein in order to absorb visible light.

Chromophores are partly unsaturated and electrons can be de-localised across a conjugated π systems by the energy of a visiblephoton. Larger chromophores with larger conjugated π system, canabsorb longer wavelengths of light. The length of a conjugated systemaffects λmax because the energy gap ∆E decreases between the highestoccupied molecular orbital (HOMO) and lowest unoccupied molecu-lar orbital (LUMO) as the number of conjugated pi bonds increases,requiring less energy to excite electrons from the π − π∗ orbital. Ex-amples of different photoreceptor families and their absorption wave-lengths across the visible light spectrum can be seen in Figure 1.3.

Over three hundred photochemically reactive proteins that use avitamin-A aldehyde (retinal) as their chromophore to absorb lighthave been reported to date. Retinylidine proteins are commonly re-ferred to as rhodopsins. All rhodopsin share a common topology whichconsists of seven membrane-embedded α-helices that form an inter-nal pocket in which the retinal is bound (Figure 1.4). Although,rhodopsins share common topology of secondary and tertiary struc-ture, primary sequence alignment split retinylidene proteins into twodistinct families. Type 1 rhodopsins, commonly known as microbialrhodopsins are found in archaea, prokayrotes and unicellular greenalgae and function as light-driven proton pumps, light-gated ion chan-nels or receptors for phototaxis. Type 2 rhodopsins or visual rhodopsins

1.2. Photoreceptor & Retinylidine Proteins

Figure 1.3: Range of photoreceptors in the electromagnetic spec-trum of visible light. Depending on the type of chromophore in a givenphotoreceptor protein, light is absorbed at different wavelengths, rangingfrom UV to near IR.

are found in higher eukaryotes and consists of the photosensitive re-ceptor proteins in animal eyes, including the human rod cells [10],which are responsible for low-light vision.

Rhodopsins are opsin apoproteins which are made light-sensitivevia the addition of retinal chromophore. Essential for the functionof all retinyldine proteins, is the retinal chromophore, covalently at-tached by a Schiff base (SB) linkage to the ε-amino group of a lysineside chain in the middle of the seventh helix (figure 2.1) [11]. Uponbinding of the retinal chromophore to the opsin apoprotein, the ab-sorption spectra of the retinal chromophore varies over a wide range-from 360 to 635 nm. This shift in the absorbance maximum is knownas an opsin shift [8], and is dependant on the protein environment inthe binding pocket. The interaction of polar protein residues [12] andsurrounding solvent with the retinal prosthetic group in the bindingpocket, alters the charge distribution of retinal which in turn allowsfor tuning the absorption maximum to display a wide variety of char-acteristic colors [13] [14]. The molecular and electrostatic environmentin the binding pocket are different between rhodopsin and the ability

1.2 Photoreceptor & Retinylidine ProteinsSensing and reacting to a wide range of environmental cues, is essentialto all life on earth. Light is one of the most important sources ofenergy and environmental signals, and many organisms need to adaptin response to the presence of light [8]. This is made possible throughspecialised proteins called photoreceptors.

Photochemically reactive proteins have been described in archaeal,prokaryotic and eukaryotic organisms, expanding across all domainsof life [9]. These proteins mediate light responses such as visual per-ception, phototropism, phototaxis, and the circadian rhythm. Manyphotoreceptor proteins use a chromophore, embedded in the core ofthe protein in order to absorb visible light.

Chromophores are partly unsaturated and electrons can be de-localised across a conjugated π systems by the energy of a visiblephoton. Larger chromophores with larger conjugated π system, canabsorb longer wavelengths of light. The length of a conjugated systemaffects λmax because the energy gap ∆E decreases between the highestoccupied molecular orbital (HOMO) and lowest unoccupied molecu-lar orbital (LUMO) as the number of conjugated pi bonds increases,requiring less energy to excite electrons from the π − π∗ orbital. Ex-amples of different photoreceptor families and their absorption wave-lengths across the visible light spectrum can be seen in Figure 1.3.

Over three hundred photochemically reactive proteins that use avitamin-A aldehyde (retinal) as their chromophore to absorb lighthave been reported to date. Retinylidine proteins are commonly re-ferred to as rhodopsins. All rhodopsin share a common topology whichconsists of seven membrane-embedded α-helices that form an inter-nal pocket in which the retinal is bound (Figure 1.4). Although,rhodopsins share common topology of secondary and tertiary struc-ture, primary sequence alignment split retinylidene proteins into twodistinct families. Type 1 rhodopsins, commonly known as microbialrhodopsins are found in archaea, prokayrotes and unicellular greenalgae and function as light-driven proton pumps, light-gated ion chan-nels or receptors for phototaxis. Type 2 rhodopsins or visual rhodopsins

1.2. Photoreceptor & Retinylidine Proteins

Figure 1.3: Range of photoreceptors in the electromagnetic spec-trum of visible light. Depending on the type of chromophore in a givenphotoreceptor protein, light is absorbed at different wavelengths, rangingfrom UV to near IR.

are found in higher eukaryotes and consists of the photosensitive re-ceptor proteins in animal eyes, including the human rod cells [10],which are responsible for low-light vision.

Rhodopsins are opsin apoproteins which are made light-sensitivevia the addition of retinal chromophore. Essential for the functionof all retinyldine proteins, is the retinal chromophore, covalently at-tached by a Schiff base (SB) linkage to the ε-amino group of a lysineside chain in the middle of the seventh helix (figure 2.1) [11]. Uponbinding of the retinal chromophore to the opsin apoprotein, the ab-sorption spectra of the retinal chromophore varies over a wide range-from 360 to 635 nm. This shift in the absorbance maximum is knownas an opsin shift [8], and is dependant on the protein environment inthe binding pocket. The interaction of polar protein residues [12] andsurrounding solvent with the retinal prosthetic group in the bindingpocket, alters the charge distribution of retinal which in turn allowsfor tuning the absorption maximum to display a wide variety of char-acteristic colors [13] [14]. The molecular and electrostatic environmentin the binding pocket are different between rhodopsin and the ability

of retinylidine proteins to tune its spectral properties has been crucialfor the evolution of rhodopsin function.

Figure 1.4: 2D topology of retinal proteins. Seven transmembraneα-helices denoted typically A-G in microbial opsins (type 1) and TM1 toTM7 in the animal opsins (type 2). Spanning across the lipid bilayer, theN- and C-terminus faces the outside and inside, respectively and retinal iscovalently bound to a lysine side chain on helix G or TM7.

Light absorption initiates functional events in all rhodopsins. En-ergy absorbed by the retinal triggers the chromophore to isomeriseacross one of the double bonds in the polyene chain, resulting inthe adoption of a new configuration. In type 1 rhodopsins, isomeri-sation causes retinal to change an all-trans to a 13-cis configuration.For type 2 rhodopsins, the ground state is 11-cis, and upon lightabsorption isomerises to an all-trans state [11] [15]. Chromophore-protein interactions guide the unique photophysical and photochem-ical processes for energy or the initiation of intra- or intercellularsignaling in rhodopsins. The starting point is the specific photoiso-merisation of retinal which culminates in distinct protein conforma-tional changes [11]. In the following chapter, we will look closer atthe structure-function relationships of four different retinylidine pro-teins; bacteriorhodopsin (bR), sensory rhodopsin II and transducer(SRII/SRIIHtrII), Channelrhodopsin-2 (ChR2) and visual rhodopsin(rho).

1.3. Scope of this thesis

1.3 Scope of this thesisThis thesis utilized time-resolved X-ray solution scattering to modelstructural changes from difference X-ray scattering observations oflight-activated integral membrane proteins solubilized within a deter-gent micelle. This work has been grouped into the following chapters.Chapter 2 will give an overview of Type I and Type II rhodpsinswhich were used in this work. Chapter 3 Will discuss protein dynam-ics within the field of structural biology, and the different biophysicalmethods that can be utilized, each with their respective strengths andweaknesses. Chapter 4 will give a brief introduction to the basics of X-ray (solution) scattering. Chapter 5 will describe how a time-resolvedexperiment is performed and also how the data can be interpreted andanalyzed in order to recover structural information from the differenceX-ray scattering curves. Chapter 6 Will discuss the results obtainedfrom structural modelling of Bacteriorhodopsin (PAPER I), SRIIin complex with it’s transducer HtrII (PAPER II) and Channel-rhodopsin 2 (PAPER III) Finally, Chapter 7 will summarize, con-clude and provide an outlook.

of retinylidine proteins to tune its spectral properties has been crucialfor the evolution of rhodopsin function.

Figure 1.4: 2D topology of retinal proteins. Seven transmembraneα-helices denoted typically A-G in microbial opsins (type 1) and TM1 toTM7 in the animal opsins (type 2). Spanning across the lipid bilayer, theN- and C-terminus faces the outside and inside, respectively and retinal iscovalently bound to a lysine side chain on helix G or TM7.

Light absorption initiates functional events in all rhodopsins. En-ergy absorbed by the retinal triggers the chromophore to isomeriseacross one of the double bonds in the polyene chain, resulting inthe adoption of a new configuration. In type 1 rhodopsins, isomeri-sation causes retinal to change an all-trans to a 13-cis configuration.For type 2 rhodopsins, the ground state is 11-cis, and upon lightabsorption isomerises to an all-trans state [11] [15]. Chromophore-protein interactions guide the unique photophysical and photochem-ical processes for energy or the initiation of intra- or intercellularsignaling in rhodopsins. The starting point is the specific photoiso-merisation of retinal which culminates in distinct protein conforma-tional changes [11]. In the following chapter, we will look closer atthe structure-function relationships of four different retinylidine pro-teins; bacteriorhodopsin (bR), sensory rhodopsin II and transducer(SRII/SRIIHtrII), Channelrhodopsin-2 (ChR2) and visual rhodopsin(rho).

1.3. Scope of this thesis

1.3 Scope of this thesisThis thesis utilized time-resolved X-ray solution scattering to modelstructural changes from difference X-ray scattering observations oflight-activated integral membrane proteins solubilized within a deter-gent micelle. This work has been grouped into the following chapters.Chapter 2 will give an overview of Type I and Type II rhodpsinswhich were used in this work. Chapter 3 Will discuss protein dynam-ics within the field of structural biology, and the different biophysicalmethods that can be utilized, each with their respective strengths andweaknesses. Chapter 4 will give a brief introduction to the basics of X-ray (solution) scattering. Chapter 5 will describe how a time-resolvedexperiment is performed and also how the data can be interpreted andanalyzed in order to recover structural information from the differenceX-ray scattering curves. Chapter 6 Will discuss the results obtainedfrom structural modelling of Bacteriorhodopsin (PAPER I), SRIIin complex with it’s transducer HtrII (PAPER II) and Channel-rhodopsin 2 (PAPER III) Finally, Chapter 7 will summarize, con-clude and provide an outlook.

Chapter 2

Microbial and animalrhodopsins

2.1 BacteriorhodopsinDiscovered a half-century ago in in the purple membrane of Halobac-terium salinarum [16], bacteriorhodopsin (bR) was the first character-ized microbial rhodopsin and is the most well studied retinylidine pro-tein. These haloarchea live in sunny, high-temperature and extremelysalt-rich brines, with maximum 20% of the oxygen levels present innormal sea water. In these brines, where oxygen is sparse, alterna-tives to respiration are crucial in order to generate energy and en-sure survival [17]. If the surrounding environment accommodates suf-ficient quantities of arginine, which is usually derived from decompos-ing organisms, then survival is achieved via fermentation [18]. Whenarginine and oxygen is lacking, H.salinarium utilizes sun light as it’sprimary source of energy, and promotes the biosynthesis of what ap-pears to be the most ancient photosynthetic system in nature; bacte-riorhodpsin [17].

H.salinarium synthesizes cell membrane containing bacteriorhodopsinin assembled, concentrated patches. These patches in the cellularmembrane are known as purple membrane [17]. Under favourable con-ditions for the synthesis of bacteriorhodpsin, the purple membrane canreach over 75% of weight of the total cell membrane area [16]. Whenilluminated, bR molecules in the purple membrane harness the light

Chapter 2

Microbial and animalrhodopsins

2.1 BacteriorhodopsinDiscovered a half-century ago in in the purple membrane of Halobac-terium salinarum [16], bacteriorhodopsin (bR) was the first character-ized microbial rhodopsin and is the most well studied retinylidine pro-tein. These haloarchea live in sunny, high-temperature and extremelysalt-rich brines, with maximum 20% of the oxygen levels present innormal sea water. In these brines, where oxygen is sparse, alterna-tives to respiration are crucial in order to generate energy and en-sure survival [17]. If the surrounding environment accommodates suf-ficient quantities of arginine, which is usually derived from decompos-ing organisms, then survival is achieved via fermentation [18]. Whenarginine and oxygen is lacking, H.salinarium utilizes sun light as it’sprimary source of energy, and promotes the biosynthesis of what ap-pears to be the most ancient photosynthetic system in nature; bacte-riorhodpsin [17].

H.salinarium synthesizes cell membrane containing bacteriorhodopsinin assembled, concentrated patches. These patches in the cellularmembrane are known as purple membrane [17]. Under favourable con-ditions for the synthesis of bacteriorhodpsin, the purple membrane canreach over 75% of weight of the total cell membrane area [16]. Whenilluminated, bR molecules in the purple membrane harness the light

Chapter 2. Microbial and animal rhodopsins

Figure 2.1: Rhodopsin topology and retinal chromophore. Left:Seven transmembrane α-helical topology of rhodopsins with the bound reti-nal chromorophore (violet). Right: All-trans retinal bound to lysine residuevia protonated Schiff base.

energy to translocate protons from the negatively charged cytoplas-mic side (inside) to the positively charged extracellular side (outside)of the cell. This creates a electrochemical proton gradient across themembrane which is in turn harvested by the enzyme ATP synthase,to synthesise the universal biological energy currency; ATP [19] [20].

Since the function of a protein is directly related to it’s structure,extensive efforts using a multitude of biophysical methods has beenused in order to elucidate the resting state of bR, as well as the struc-tures of it’s photo-cycle intermediates. Shortly after it’s discovery, bRwas functionally characterized as a light-driven proton pump [21–24],and in 1975, electron diffraction studies of naturally occurring 2Dcrystals from bR, gave the first structural insights into the arrange-ment of transmembrane helices in a membrane protein [25]. In the year1990, the first atomic model of bR was recovered from electron cryo-microscopy [26]. Since this time, over one hundred reported structuresof bR have been deposited [27], and the culmination of the vast spec-troscopic studies gives rise to the emergence of a general description

2.1. Bacteriorhodopsin

of the sequence of generated intermediates during the photocycle ofbR.

The key residues - Asp85, Arg82, Asp96, Glu194, Glu204, Asp212,and Lys216 form a proton transfer channel through the center of theprotein (see fig 2.2).

Local structural changes in the chemical environment of thesecharged amino acids influences their pKa values, and protons areexchanged between these charged amino acids as a result of thesefluctuations in affinity [30]. In the resting state of bR, the all-transretinal is covalently bound to Lys216 in helix G through a protonatedSB which acts as a primary donor. The photo cycle is initiated whenthe resting state absorbs a photon, leading to the rapid isomerisationof retinal from an all-trans to 13-cis configuration. This is initiates aseries of structural rearrangements in the chromophore environmentwhich correlates with a sequence of spectrally distinct intermediatesreferred to as I,J,K,L,M,N and O (see fig 2.2).

Time-resolved spectroscopy has shown the rise and decay of in-termediates I-,J, and K within the first picoseconds following pho-toactivation [31–34]. These ultrafast structural changes has been ob-served using TR-SFX [35]. After a few picoseconds, the red-shiftedK-intermediate is formed, where the isomerized retinal reorients theSB towards the cytoplasmic side which breaks key interactions withthe complex counter ion, and accessibility to Asp85 is temporarilydecreased. The blue-shifted L-intermediate is formed on a time-scaleof microseconds and the local inward bend of helix C towards helixG brings Asp85 in closer proximity to the SB and raises the pKa ofAsp85 to the point where it can spontaneously accept a proton fromthe SB [36]. A transient proton-transport pathway linking the SB toAsp85 via Thr89 aids in the deprotonation step [36]. The key stepin the unidirectional proton transport in bR is the primary protontransfer event from the SB to Asp85, which occurs on the tens ofmicrosecond time-scale (L-M1 intermediate). On the same time-scale,the release of a proton into the extracellular medium, coincides withthe formation of the M1 intermediate [37]. As the M-state spectraltransition is formed, the positively charged head group of Arg82 is

Figure 2.1: Rhodopsin topology and retinal chromophore. Left:Seven transmembrane α-helical topology of rhodopsins with the bound reti-nal chromorophore (violet). Right: All-trans retinal bound to lysine residuevia protonated Schiff base.

energy to translocate protons from the negatively charged cytoplas-mic side (inside) to the positively charged extracellular side (outside)of the cell. This creates a electrochemical proton gradient across themembrane which is in turn harvested by the enzyme ATP synthase,to synthesise the universal biological energy currency; ATP [19] [20].

Since the function of a protein is directly related to it’s structure,extensive efforts using a multitude of biophysical methods has beenused in order to elucidate the resting state of bR, as well as the struc-tures of it’s photo-cycle intermediates. Shortly after it’s discovery, bRwas functionally characterized as a light-driven proton pump [21–24],and in 1975, electron diffraction studies of naturally occurring 2Dcrystals from bR, gave the first structural insights into the arrange-ment of transmembrane helices in a membrane protein [25]. In the year1990, the first atomic model of bR was recovered from electron cryo-microscopy [26]. Since this time, over one hundred reported structuresof bR have been deposited [27], and the culmination of the vast spec-troscopic studies gives rise to the emergence of a general description

2.1. Bacteriorhodopsin

of the sequence of generated intermediates during the photocycle ofbR.

The key residues - Asp85, Arg82, Asp96, Glu194, Glu204, Asp212,and Lys216 form a proton transfer channel through the center of theprotein (see fig 2.2).

Local structural changes in the chemical environment of thesecharged amino acids influences their pKa values, and protons areexchanged between these charged amino acids as a result of thesefluctuations in affinity [30]. In the resting state of bR, the all-transretinal is covalently bound to Lys216 in helix G through a protonatedSB which acts as a primary donor. The photo cycle is initiated whenthe resting state absorbs a photon, leading to the rapid isomerisationof retinal from an all-trans to 13-cis configuration. This is initiates aseries of structural rearrangements in the chromophore environmentwhich correlates with a sequence of spectrally distinct intermediatesreferred to as I,J,K,L,M,N and O (see fig 2.2).

Time-resolved spectroscopy has shown the rise and decay of in-termediates I-,J, and K within the first picoseconds following pho-toactivation [31–34]. These ultrafast structural changes has been ob-served using TR-SFX [35]. After a few picoseconds, the red-shiftedK-intermediate is formed, where the isomerized retinal reorients theSB towards the cytoplasmic side which breaks key interactions withthe complex counter ion, and accessibility to Asp85 is temporarilydecreased. The blue-shifted L-intermediate is formed on a time-scaleof microseconds and the local inward bend of helix C towards helixG brings Asp85 in closer proximity to the SB and raises the pKa ofAsp85 to the point where it can spontaneously accept a proton fromthe SB [36]. A transient proton-transport pathway linking the SB toAsp85 via Thr89 aids in the deprotonation step [36]. The key stepin the unidirectional proton transport in bR is the primary protontransfer event from the SB to Asp85, which occurs on the tens ofmicrosecond time-scale (L-M1 intermediate). On the same time-scale,the release of a proton into the extracellular medium, coincides withthe formation of the M1 intermediate [37]. As the M-state spectraltransition is formed, the positively charged head group of Arg82 is

Figure 2.2: Overview of the structure and function of bacteri-orhodopsin (bR). Left: Structure of bR highlighting key residues thatparticipate in proton transfer steps in bR. The bR ground state is repre-sented as a purple ribbon, with helices A to G labeled. Arrows indicate theproton-transfer steps between charged residues. Each arrow is numbered,indicating the sequence in which the proton-transfer steps occur. The pri-mary proton transfer (1) is from the SB to Asp85, followed by relase of aproton by the release group Glu194 and Glu204 into the EC medium (2).The SB is subsequently reprotonated from Asp96 (3), which itself is re-potonated from the CP medium (4). The final step in the proton transfer(5) includes the proton release from Asp85 to the release group via Arg82,consequently restoring the ground state ( [28,29]). Right: Overview of spec-tral changes in the bR photocycle. Spectral intermediates are labeled fromI to O, their absorption maxima, and the approximate timescales for theirgrowth and decay are shown.

2.2. Sensory rhodopsin II and transducer

moved toward the EC, which is triggered by the protonation of Asp85,resulting in a increase of the pKa of the primary proton acceptor, andthus stopping reverse proton transfer [38]. These perturbations trig-ger rearrangements of Glu194 and Glu204, and influence the releaseof a proton into the EC medium [38, 39]. In the submillisecond timerange, the early M state (M1) transitions into the late M intermediate(M2) [40] and has been correlated with a large structural rearrange-ment on the cytoplasmic side [41]. This changes the accessibility of theretinal, so that the Schiff base can subsequently be reprotonated fromAsp96 on the CP side [42]. Recent time-resolved serial synchrotronX-ray crystallography (TR-SSX) confirmed that an E-F helix open-ing, connecting a proton wire via Asp96 to the retinal SB, definesthe M - N transition, in the millisecond time range [43]. The observa-tion of larger structural changes showed discrepancies across studies,owing partly to crystal packing restricting these motions [36]. Theretinal is isomerized back to the all-trans configuration, associatedwith the transition from the N- to the O-intermediate in the millisec-ond time-scale. Lastly, the ground state is recovered when a proton istransferred from Asp85 to the putative release group on EC side viaArg82, marking the end of the photocycle [28].

2.2 Sensory rhodopsin II and transducerIn haloarchea, sensor rhodopsin I and sensory rhodopsin II (SRI andSRII) are phototaxis receptors that work in tandem to control thecell’s swimming behaviour, allowing the directed movement away fromharmful light as a response to changes in light intensity and color.Motility is afforded by SRI and SRII using the energy of a photonto send signals to the flagellar motor via their complexed transducerprotein HtrI and HtrII [44]. In the haloarchea, H.salinarum, when oxy-gen levels are high, the cells avoid sunlight which can cause photoox-idative damage. In these conditions, H.salinarium synthesizes solelySRII, also known as phoborhodpsin. SRII absorbs blue-green light inthe visible light spectrum, at ∼498nm, and it’s tuned to seek the dark.When oxygen levels drop, SRII production is suppressed, and instead,

Figure 2.2: Overview of the structure and function of bacteri-orhodopsin (bR). Left: Structure of bR highlighting key residues thatparticipate in proton transfer steps in bR. The bR ground state is repre-sented as a purple ribbon, with helices A to G labeled. Arrows indicate theproton-transfer steps between charged residues. Each arrow is numbered,indicating the sequence in which the proton-transfer steps occur. The pri-mary proton transfer (1) is from the SB to Asp85, followed by relase of aproton by the release group Glu194 and Glu204 into the EC medium (2).The SB is subsequently reprotonated from Asp96 (3), which itself is re-potonated from the CP medium (4). The final step in the proton transfer(5) includes the proton release from Asp85 to the release group via Arg82,consequently restoring the ground state ( [28,29]). Right: Overview of spec-tral changes in the bR photocycle. Spectral intermediates are labeled fromI to O, their absorption maxima, and the approximate timescales for theirgrowth and decay are shown.

moved toward the EC, which is triggered by the protonation of Asp85,resulting in a increase of the pKa of the primary proton acceptor, andthus stopping reverse proton transfer [38]. These perturbations trig-ger rearrangements of Glu194 and Glu204, and influence the releaseof a proton into the EC medium [38, 39]. In the submillisecond timerange, the early M state (M1) transitions into the late M intermediate(M2) [40] and has been correlated with a large structural rearrange-ment on the cytoplasmic side [41]. This changes the accessibility of theretinal, so that the Schiff base can subsequently be reprotonated fromAsp96 on the CP side [42]. Recent time-resolved serial synchrotronX-ray crystallography (TR-SSX) confirmed that an E-F helix open-ing, connecting a proton wire via Asp96 to the retinal SB, definesthe M - N transition, in the millisecond time range [43]. The observa-tion of larger structural changes showed discrepancies across studies,owing partly to crystal packing restricting these motions [36]. Theretinal is isomerized back to the all-trans configuration, associatedwith the transition from the N- to the O-intermediate in the millisec-ond time-scale. Lastly, the ground state is recovered when a proton istransferred from Asp85 to the putative release group on EC side viaArg82, marking the end of the photocycle [28].

2.2 Sensory rhodopsin II and transducerIn haloarchea, sensor rhodopsin I and sensory rhodopsin II (SRI andSRII) are phototaxis receptors that work in tandem to control thecell’s swimming behaviour, allowing the directed movement away fromharmful light as a response to changes in light intensity and color.Motility is afforded by SRI and SRII using the energy of a photonto send signals to the flagellar motor via their complexed transducerprotein HtrI and HtrII [44]. In the haloarchea, H.salinarum, when oxy-gen levels are high, the cells avoid sunlight which can cause photoox-idative damage. In these conditions, H.salinarium synthesizes solelySRII, also known as phoborhodpsin. SRII absorbs blue-green light inthe visible light spectrum, at ∼498nm, and it’s tuned to seek the dark.When oxygen levels drop, SRII production is suppressed, and instead,

synthesis of SRI, bR and HR is induced, in order for the cell to uti-lize light absorption for energy production via it’s proton pumps. SRIhas two modes, responding to two different wavelengths of light. Thephoto-attractive signal responds to orange light (∼565 nm), and me-diates the migration into illuminated regions, where the ion pumpscan produce ATP using light. The second mode of SRI is a photo-phobic response, a photo-intermediate called s373, absorbs near-UVlight (∼370 nm), and mediates a strong repellent response, ensuringthat cells do not migrate to regions with higher energy light, and risk-ing damage [44–48]. The two receptors work together to produce anoverall transition from photo- attractive to photophobic signals as thelight environment moves into the UV range.

SRI and SRII both require transducer proteins to transmit signals.SRI and SRII form 2:2 complexes with their respective transducers,HtrI and HtrII in the membrane, with high binding affinity [49–51](figure 2.3).

HtrI and HtrII for SRI and SRII share homology with bacterialchemotaxis receptors, and SRII are 500-700 amino acids in length,comprising of two transmembrane helices extending into the cyto-plasm, that binds to histidine kinase CheA. Upon activation, a light isabsorbed by the retinal, isomerizing the chromophore, inducing struc-tural change and propagating from the rhodopsin to the transducer.This signal causes a conformational shift in the transducer in SRII-HtrII [52], which relays a signal from the receptor to cytoplasmicend of the molecule, where a cascade involving binding and phospho-rylation of histidine kinases, functions as a switch for the flagellarmotor [53]. SRI and SRII are structurally very similar to that of theproton pump bR [54], in fact, phylogenic analysis strongly indicatethat SRII developed from bR [55,56] after gene duplication [57], andthat gene duplication of SRII gave rise to SRI [53]. Furthermore, in theabsence of its transducer SRI and SRII show residual proton pump-ing [58–60]. Inversely, attempts to convert bR into a sensory receptorrequired only 3 mutations to confer basic signalling function, A215Tbeing the most significant [59]. Thus, only small simple conversionsmarks the essential differences between transport and signaling in therhodopsin family, and that large differences in structural mechanism

Figure 2.3: General topolgy of SRII in complex with it’s trans-ducer HtrII Structure of SRII-HtrII complex. SRII is colored orange, andhelices labled A-G, whereas HtrII transducer is represented in blue. Sideand top view is showing the relative positions of all helices in the complex.

is improbable.

Insight into the SRII photocycle is relevant to it’s functionalityand dynamics, and has been vigorously studied. The essence of theSRII photocycle can be explained as; SRII(487) → M(360) → O(525)→ SRII(487) [61]. This is because The M and O intermediates areconsidered to be signaling state(s) for the photo-repellent cell behavior[62]. A more detailed description of the SRII photocycle can be seenin figure 2.4.

Retinal isomerisation is the main driving force behind SRII func-tion, and femtosecond spectroscopy shows that although isomerisationis similar to bR and hR, the post-isomerisation response differs, whichis consistent with a model for signal transduction. A steric constraintbetween retinal C14 and the side chain of Thr204 is a key feature when

synthesis of SRI, bR and HR is induced, in order for the cell to uti-lize light absorption for energy production via it’s proton pumps. SRIhas two modes, responding to two different wavelengths of light. Thephoto-attractive signal responds to orange light (∼565 nm), and me-diates the migration into illuminated regions, where the ion pumpscan produce ATP using light. The second mode of SRI is a photo-phobic response, a photo-intermediate called s373, absorbs near-UVlight (∼370 nm), and mediates a strong repellent response, ensuringthat cells do not migrate to regions with higher energy light, and risk-ing damage [44–48]. The two receptors work together to produce anoverall transition from photo- attractive to photophobic signals as thelight environment moves into the UV range.

SRI and SRII both require transducer proteins to transmit signals.SRI and SRII form 2:2 complexes with their respective transducers,HtrI and HtrII in the membrane, with high binding affinity [49–51](figure 2.3).

HtrI and HtrII for SRI and SRII share homology with bacterialchemotaxis receptors, and SRII are 500-700 amino acids in length,comprising of two transmembrane helices extending into the cyto-plasm, that binds to histidine kinase CheA. Upon activation, a light isabsorbed by the retinal, isomerizing the chromophore, inducing struc-tural change and propagating from the rhodopsin to the transducer.This signal causes a conformational shift in the transducer in SRII-HtrII [52], which relays a signal from the receptor to cytoplasmicend of the molecule, where a cascade involving binding and phospho-rylation of histidine kinases, functions as a switch for the flagellarmotor [53]. SRI and SRII are structurally very similar to that of theproton pump bR [54], in fact, phylogenic analysis strongly indicatethat SRII developed from bR [55,56] after gene duplication [57], andthat gene duplication of SRII gave rise to SRI [53]. Furthermore, in theabsence of its transducer SRI and SRII show residual proton pump-ing [58–60]. Inversely, attempts to convert bR into a sensory receptorrequired only 3 mutations to confer basic signalling function, A215Tbeing the most significant [59]. Thus, only small simple conversionsmarks the essential differences between transport and signaling in therhodopsin family, and that large differences in structural mechanism

Figure 2.3: General topolgy of SRII in complex with it’s trans-ducer HtrII Structure of SRII-HtrII complex. SRII is colored orange, andhelices labled A-G, whereas HtrII transducer is represented in blue. Sideand top view is showing the relative positions of all helices in the complex.

is improbable.

Insight into the SRII photocycle is relevant to it’s functionalityand dynamics, and has been vigorously studied. The essence of theSRII photocycle can be explained as; SRII(487) → M(360) → O(525)→ SRII(487) [61]. This is because The M and O intermediates areconsidered to be signaling state(s) for the photo-repellent cell behavior[62]. A more detailed description of the SRII photocycle can be seenin figure 2.4.

Retinal isomerisation is the main driving force behind SRII func-tion, and femtosecond spectroscopy shows that although isomerisationis similar to bR and hR, the post-isomerisation response differs, whichis consistent with a model for signal transduction. A steric constraintbetween retinal C14 and the side chain of Thr204 is a key feature when

Figure 2.4: SRII photocycle The SRII photocycle intermediates, show-ing the rise and decay of different species based on various time-resolvedmeasurements

it comes to the efficiency of the phototaxis response. This constraintcauses a series of pertubations to the hydrogen bonding network be-tween residues Tyr174, Thr204 and Leu200, and is transfered to Asn74on TM2 of HtrII via Tyr199 on helix-G [53]. Mutation of Thr204 orTyr174, abolishes the signal transduction, as the initial perturbationin the binding pocket that drives the signal to the transducer . Theseresidues are essential for phototaxis signaling, and the interaction be-tween them upon retinal isomerisation is fundamental for the signaltransduction between activated NpSRII and NpHtrII [53].

Like other rhodopsins, activation causes tertiary structural changesand outward helical shifts [59, 63], and that these outward move-ments interacts with the adjacent transducer [64]. The binding of

2.3. Channelrhodopsin

transducer seems to affect the decay of the M state in the SRII-HtrII photocycle, by slightly slowing it down [53]. Recent studies,have elucidated new mechanisms for signal transduction.The authorsdescribed a new U form in contrast to the V form previously pub-lished of NpSRII/NpSRII-HtrII [65], as a result of crystal packing,and proposed that this might be crucial for the mechanism of sig-nal propagation. The transducer is pushed upwards, like piston-likeshift towards the cytosol, in contrast to the rotational twist previouslysuggested [63,66], and may suggest that the E-F helical loop plays animportant role in the receptor-transducer interface [67].

2.3 ChannelrhodopsinAltering the the very fine tuned structural template of bR-like pro-ton pumps can afford ion pumps with different ion specificities suchas chloride and sodium [68, 69]. These function-altering changes cannot only be derived by evolution, but have also been achieved by mu-tation of key residues in vitro [70, 71]. Halorhodopsin, was initallyidentified in Halobacterium salinarum in 1977 [72] from pH measure-ments, suggesting initially that there is bR-like protein that differsfrom that in the purple membrane and later shown to be an inwarddirected chloride (Cl-) pump, transporting Cl- ions in the cytoplasmicdirection [68]. This Cl- pump has an absorbance maxima at 575 nm,and a photocycle lasting 20 ms, with the rise and decay of severalphotocycle intermediates [73]. The hR pumps Cl- ions in the inversedirection to the proton pumping in bR (fig 2.5). Even though hR andbR share similar architecture, crystal structures of hR resting stateshows the presence of a Cl- ion in close proximy to the retinal schiffbase (RSB) where it occupies the position of Asp85 carboxylate inbR, trapped halfway through the membrane [74,75]. Isomerisation ofthe retinal shifts the protonated SB dipole, and pulls the Cl- ion pastthe retinal [74, 76], and unidirectional anion transport is achieved bythe inward flex of helix-C, which prevents further uptake of Cl- ionsduring excitation [77].

Figure 2.4: SRII photocycle The SRII photocycle intermediates, show-ing the rise and decay of different species based on various time-resolvedmeasurements

it comes to the efficiency of the phototaxis response. This constraintcauses a series of pertubations to the hydrogen bonding network be-tween residues Tyr174, Thr204 and Leu200, and is transfered to Asn74on TM2 of HtrII via Tyr199 on helix-G [53]. Mutation of Thr204 orTyr174, abolishes the signal transduction, as the initial perturbationin the binding pocket that drives the signal to the transducer . Theseresidues are essential for phototaxis signaling, and the interaction be-tween them upon retinal isomerisation is fundamental for the signaltransduction between activated NpSRII and NpHtrII [53].

Like other rhodopsins, activation causes tertiary structural changesand outward helical shifts [59, 63], and that these outward move-ments interacts with the adjacent transducer [64]. The binding of

2.3. Channelrhodopsin

transducer seems to affect the decay of the M state in the SRII-HtrII photocycle, by slightly slowing it down [53]. Recent studies,have elucidated new mechanisms for signal transduction.The authorsdescribed a new U form in contrast to the V form previously pub-lished of NpSRII/NpSRII-HtrII [65], as a result of crystal packing,and proposed that this might be crucial for the mechanism of sig-nal propagation. The transducer is pushed upwards, like piston-likeshift towards the cytosol, in contrast to the rotational twist previouslysuggested [63,66], and may suggest that the E-F helical loop plays animportant role in the receptor-transducer interface [67].

2.3 ChannelrhodopsinAltering the the very fine tuned structural template of bR-like pro-ton pumps can afford ion pumps with different ion specificities suchas chloride and sodium [68, 69]. These function-altering changes cannot only be derived by evolution, but have also been achieved by mu-tation of key residues in vitro [70, 71]. Halorhodopsin, was initallyidentified in Halobacterium salinarum in 1977 [72] from pH measure-ments, suggesting initially that there is bR-like protein that differsfrom that in the purple membrane and later shown to be an inwarddirected chloride (Cl-) pump, transporting Cl- ions in the cytoplasmicdirection [68]. This Cl- pump has an absorbance maxima at 575 nm,and a photocycle lasting 20 ms, with the rise and decay of severalphotocycle intermediates [73]. The hR pumps Cl- ions in the inversedirection to the proton pumping in bR (fig 2.5). Even though hR andbR share similar architecture, crystal structures of hR resting stateshows the presence of a Cl- ion in close proximy to the retinal schiffbase (RSB) where it occupies the position of Asp85 carboxylate inbR, trapped halfway through the membrane [74,75]. Isomerisation ofthe retinal shifts the protonated SB dipole, and pulls the Cl- ion pastthe retinal [74, 76], and unidirectional anion transport is achieved bythe inward flex of helix-C, which prevents further uptake of Cl- ionsduring excitation [77].

Channelrhodopsins (ChR), are a famility of cationic specific light-gated ion channels. Channelrhodopsin-2 (ChR2) is a Ca2+ specificion channel. Extensive research on the swimming behaviour of motilemicroalgea Euglena gracilis and Chlamydomonas reinhardtii laid thefoundations for the discovery of channelrhodopsin [11]. This unicellu-lar microalgea senses light to adapt flaggelar beating for swimming,and in the 1950’s it was discovered that this behavioural response wasdependant on Mg2+ and Ca2+ [78] and two decades later, it was foundthat Ca2+ influx was crucial for this flagellar swimming behaviour [79].Several years later, rhodopsin genes from Chlamydomonas was foundto encode light-gated ion channels; channel-rhodopsin-1 (ChR1) andchannelrhodopsin-2 (ChR2) [80,81].

With the proposal that for neuronal cells, the expression of ChR2might allow depolarization of the cells by illumination [81], severalstudies was conducted to prove this concept [82–87], leading ChR2into the field of neuroscience and the birth of optogenetics [11]. In thefield of optogenetics, scientists can use Chr2 expressed in neural cellpopulations to selectively activate or silence (depolarize or hyperpo-larize) cells, using short pulses of light. The field of optogenetics withthe help of ChR2 allows for the study of the nervous system and thepursuite for assembling a general theory of the mind [88].

The structure of Chr2 was recently solved to a high resolution [89],and identified ion conduction pathway cavities which are separatedby three gates: a central gate around the retinal chromophore, anda intra- and extracellular gate. The ordered water molecules presentin the retinal binding pocket of bR and SRII are not observed inChR2, and similar water coordination occurs instead via E123 andD253 and the SB. These residues participate in an extensive hydrogenbonding network that stabilize the extracellular- and central gate.Upon isomerisation, these two gates are opened, and the extensivehydrogen bonding network is disrupted leading to structural changesin helix B,F,G and the opening of the intracellular gate and channelpore formation, allowing for cation transfer [89].

2.4. Visual rhodopsin

Figure 2.5: Type 1 Microbial Rhodopsins

2.4 Visual rhodopsinIn contrast to the previously discussed Type 1 microbial rhodopsins,the Type 2 rhodopsins or visual rhodopsins are found in higher eu-karyotes and was the first G-protein-coupled receptor (GPCR) to beresolved by crystallography [90]. The retinal rod cells known as aphotoreceptor cells, are specialized neurons responsible for detectinglight in the vertebrate eye. Found in the retina, these cells capture andconvert light into a chemical signal in neurons, marking the first stepin the process of night-vision. The rod cells consist of an outer andinner segment, where the inner segment encloses the nucleus and mi-tochondria, whereas the rod outer segment (ROS) contains rhodopsinsand complementary proteins that harvest and amplify the photon sig-nal [91]. The sensitivity is so great, that even a single photon can bedetected [92] The ROS consists of 1000-2000 stacked discs containingaround 8x104 rhodopsin molecules per disk,and the remainder is filledwith phospholipids and cholesterol [2]. Ion channels (Na+ and Ca2+)in the outer segment open and close depending on cyclic guanosinemonophosphate concentration (cGMP). Upon light absorption by theouter segment, cGMP levels drops in the rod cell causing the cGMPactivated ion channels to close and dramatically alters the membrane

Channelrhodopsins (ChR), are a famility of cationic specific light-gated ion channels. Channelrhodopsin-2 (ChR2) is a Ca2+ specificion channel. Extensive research on the swimming behaviour of motilemicroalgea Euglena gracilis and Chlamydomonas reinhardtii laid thefoundations for the discovery of channelrhodopsin [11]. This unicellu-lar microalgea senses light to adapt flaggelar beating for swimming,and in the 1950’s it was discovered that this behavioural response wasdependant on Mg2+ and Ca2+ [78] and two decades later, it was foundthat Ca2+ influx was crucial for this flagellar swimming behaviour [79].Several years later, rhodopsin genes from Chlamydomonas was foundto encode light-gated ion channels; channel-rhodopsin-1 (ChR1) andchannelrhodopsin-2 (ChR2) [80,81].

With the proposal that for neuronal cells, the expression of ChR2might allow depolarization of the cells by illumination [81], severalstudies was conducted to prove this concept [82–87], leading ChR2into the field of neuroscience and the birth of optogenetics [11]. In thefield of optogenetics, scientists can use Chr2 expressed in neural cellpopulations to selectively activate or silence (depolarize or hyperpo-larize) cells, using short pulses of light. The field of optogenetics withthe help of ChR2 allows for the study of the nervous system and thepursuite for assembling a general theory of the mind [88].

The structure of Chr2 was recently solved to a high resolution [89],and identified ion conduction pathway cavities which are separatedby three gates: a central gate around the retinal chromophore, anda intra- and extracellular gate. The ordered water molecules presentin the retinal binding pocket of bR and SRII are not observed inChR2, and similar water coordination occurs instead via E123 andD253 and the SB. These residues participate in an extensive hydrogenbonding network that stabilize the extracellular- and central gate.Upon isomerisation, these two gates are opened, and the extensivehydrogen bonding network is disrupted leading to structural changesin helix B,F,G and the opening of the intracellular gate and channelpore formation, allowing for cation transfer [89].

Figure 2.5: Type 1 Microbial Rhodopsins

2.4 Visual rhodopsinIn contrast to the previously discussed Type 1 microbial rhodopsins,the Type 2 rhodopsins or visual rhodopsins are found in higher eu-karyotes and was the first G-protein-coupled receptor (GPCR) to beresolved by crystallography [90]. The retinal rod cells known as aphotoreceptor cells, are specialized neurons responsible for detectinglight in the vertebrate eye. Found in the retina, these cells capture andconvert light into a chemical signal in neurons, marking the first stepin the process of night-vision. The rod cells consist of an outer andinner segment, where the inner segment encloses the nucleus and mi-tochondria, whereas the rod outer segment (ROS) contains rhodopsinsand complementary proteins that harvest and amplify the photon sig-nal [91]. The sensitivity is so great, that even a single photon can bedetected [92] The ROS consists of 1000-2000 stacked discs containingaround 8x104 rhodopsin molecules per disk,and the remainder is filledwith phospholipids and cholesterol [2]. Ion channels (Na+ and Ca2+)in the outer segment open and close depending on cyclic guanosinemonophosphate concentration (cGMP). Upon light absorption by theouter segment, cGMP levels drops in the rod cell causing the cGMPactivated ion channels to close and dramatically alters the membrane

potential, which hyperpolarizes the cell to signal the optic nerve.The effiency and general steps of signal amplification and trans-

duction can be summarised in the following steps: (a) A single pho-ton can activate visual rhodospin, by triggering the conversion of abound 11-cis-retinal to all-trans-retinal causing structural changes,and allows rhodopsin to interact with its trimeric G-protein calledtransducin (Gt), where each activated rhodopsin can activate >500molecules of transducin . (b) In the inactive state transducin is boundto GDP. Activated rhodopsin interacting with transducin allows theexchange it’s GDP for GTP on the α-subunit. (c) Each molecule ofGTP bound transducin can activate a molecule of the enzyme cGMPphosphodiesterase. (d) each molecule of the enzyme cGMP phosphodi-esterase can hydroloze >4000 cGMP molecules per second to 5’-GMP.(e) Three cGMP molecules are required to open one ion channel,and drastic change of cGMP concentration as a result of hydroly-sis closes ion channels in the rod cell which leads to hyperpolariza-tion [10, 11, 44, 91, 93–96]. Remarkably, a single photon absorbed byrhodopsin yields such great amplification, that within milliseconds,more than 1000 ions channels can be closed and shifting the mem-brane potential by 1 mV [2].

Figure 2.6: Schematic illustration of phototransduction in mam-malian eyes Image directly copied from Shichida,Y & Matsuyama, T [94],with permission from The Royal Society Publishing.

Even though the general topology of 7TM architecture is shared bymicrobial and animal rhodopsins, the primary and tertiary strucutresdiffers largely [97]. The activation and photocycle of rhodopsin hasbeen extensively studied, with spectroscopially detected intermedi-ates. The protonated SB in rhodopsin is covalently bound to Lys296in TM7 and the 11-cis-retinal. Upon photon absorption and isomer-ization of the chromophore, a highly strained and distorted confor-mation of retinal is produced (photorhodopsin) within femtosecondsof activation. Through thermal relaxation, a series of photochemicalintermediates are afforded with distinct λmax values observed withlow-temperature or time-resolved spectroscopy [98, 99]. As the strainis lowered in the retinal, the bathorhodpsin, blue-shifted BSI, lumi-norhodopsin intermediates lead us to form the all-trans conformationin the Meta I intermediate in the micro second time-scale [100]. Upuntil now the SB remains protonated, probably due to low pKa ofthe stabilizing counter ion Glu113 [101]. Transition from Meta I toMeta II happens on the scale of milliseconds and includes large con-formational changes, mostly towards the cytoplasmic side [102–105],and is correlated with the SB deprotonation [106]. The formation ofthe Meta II intermediate marks the active receptor state which caninteract with it’s G-protein transducin. Over minutes the Meta II de-cays, either directly or via the reprotonated Meta III intermediate,into the apoprotein opsin with a free all-trans-retinal due to the irre-versible hydrolysis of the SB linkage. The retinal is not regenerated byreisomerisation like the Type 1 rhodopsin, but is instead reduced toretinol by retinol dehydrogenase and transported out of the photore-ceptor cell to be regenerated to 11-cis-retinal. and again slowly takenup by opsin to regenerate new rhodopsin molecules ready to absorb anew photon of light [107].

potential, which hyperpolarizes the cell to signal the optic nerve.The effiency and general steps of signal amplification and trans-

duction can be summarised in the following steps: (a) A single pho-ton can activate visual rhodospin, by triggering the conversion of abound 11-cis-retinal to all-trans-retinal causing structural changes,and allows rhodopsin to interact with its trimeric G-protein calledtransducin (Gt), where each activated rhodopsin can activate >500molecules of transducin . (b) In the inactive state transducin is boundto GDP. Activated rhodopsin interacting with transducin allows theexchange it’s GDP for GTP on the α-subunit. (c) Each molecule ofGTP bound transducin can activate a molecule of the enzyme cGMPphosphodiesterase. (d) each molecule of the enzyme cGMP phosphodi-esterase can hydroloze >4000 cGMP molecules per second to 5’-GMP.(e) Three cGMP molecules are required to open one ion channel,and drastic change of cGMP concentration as a result of hydroly-sis closes ion channels in the rod cell which leads to hyperpolariza-tion [10, 11, 44, 91, 93–96]. Remarkably, a single photon absorbed byrhodopsin yields such great amplification, that within milliseconds,more than 1000 ions channels can be closed and shifting the mem-brane potential by 1 mV [2].

Figure 2.6: Schematic illustration of phototransduction in mam-malian eyes Image directly copied from Shichida,Y & Matsuyama, T [94],with permission from The Royal Society Publishing.

Even though the general topology of 7TM architecture is shared bymicrobial and animal rhodopsins, the primary and tertiary strucutresdiffers largely [97]. The activation and photocycle of rhodopsin hasbeen extensively studied, with spectroscopially detected intermedi-ates. The protonated SB in rhodopsin is covalently bound to Lys296in TM7 and the 11-cis-retinal. Upon photon absorption and isomer-ization of the chromophore, a highly strained and distorted confor-mation of retinal is produced (photorhodopsin) within femtosecondsof activation. Through thermal relaxation, a series of photochemicalintermediates are afforded with distinct λmax values observed withlow-temperature or time-resolved spectroscopy [98, 99]. As the strainis lowered in the retinal, the bathorhodpsin, blue-shifted BSI, lumi-norhodopsin intermediates lead us to form the all-trans conformationin the Meta I intermediate in the micro second time-scale [100]. Upuntil now the SB remains protonated, probably due to low pKa ofthe stabilizing counter ion Glu113 [101]. Transition from Meta I toMeta II happens on the scale of milliseconds and includes large con-formational changes, mostly towards the cytoplasmic side [102–105],and is correlated with the SB deprotonation [106]. The formation ofthe Meta II intermediate marks the active receptor state which caninteract with it’s G-protein transducin. Over minutes the Meta II de-cays, either directly or via the reprotonated Meta III intermediate,into the apoprotein opsin with a free all-trans-retinal due to the irre-versible hydrolysis of the SB linkage. The retinal is not regenerated byreisomerisation like the Type 1 rhodopsin, but is instead reduced toretinol by retinol dehydrogenase and transported out of the photore-ceptor cell to be regenerated to 11-cis-retinal. and again slowly takenup by opsin to regenerate new rhodopsin molecules ready to absorb anew photon of light [107].

Chapter 3

Protein dynamics

3.1 Time resolved structural biologyThe function of a protein is closely related to its three dimensionalstructure. The addition of dynamics to the structure-function paradigmis crucial for a comprehensive understanding of a proteins mechanismof action [108, 109]. Proteins oscillate around an average structure,both at the resting state and when activated. These motions can bedivided into two categories; equilibrium fluctuations, and functionallyrelevant motions (figure 3.1) [110]. Equilibrium fluctuations are smallconformational rearrangements that are occur constantly such as theresting state. In traditional crystallography this is partly describedby the Debye-Waller or B-factor, which contains information aboutthe vibrational motions of atoms as a function of temperature flucta-tions [111, 112]. Functionally relevant motions require the protein tosurpass large energy barriers which is correlated with the activatedprotein, and may be induced by absorbing the energy of light, tem-perature, pH or the binding of a ligand. The study of these motions iscomplex, and different biophysical methods have been applied, eachwith their own limitations.

The first protein structure to be solved was myoglobin in 1958[113]. Since then, almost 160 000 3D protein structures have beensolved [114], where only 1300 of them are membrane proteins [115].

Chapter 3

Protein dynamics

3.1 Time resolved structural biologyThe function of a protein is closely related to its three dimensionalstructure. The addition of dynamics to the structure-function paradigmis crucial for a comprehensive understanding of a proteins mechanismof action [108, 109]. Proteins oscillate around an average structure,both at the resting state and when activated. These motions can bedivided into two categories; equilibrium fluctuations, and functionallyrelevant motions (figure 3.1) [110]. Equilibrium fluctuations are smallconformational rearrangements that are occur constantly such as theresting state. In traditional crystallography this is partly describedby the Debye-Waller or B-factor, which contains information aboutthe vibrational motions of atoms as a function of temperature flucta-tions [111, 112]. Functionally relevant motions require the protein tosurpass large energy barriers which is correlated with the activatedprotein, and may be induced by absorbing the energy of light, tem-perature, pH or the binding of a ligand. The study of these motions iscomplex, and different biophysical methods have been applied, eachwith their own limitations.

The first protein structure to be solved was myoglobin in 1958[113]. Since then, almost 160 000 3D protein structures have beensolved [114], where only 1300 of them are membrane proteins [115].

Chapter 3. Protein dynamics

Figure 3.1: Free energy landscape of a protein Resting state equi-librium fluctuations are constanstly oscillating between substates of simi-lar energy. These local flexibilities occur typically on a pico- to nanosec-ond timescale. Functionally relevant motions occur on longer timescales ofmicro- to milliseconds and requires surpassing large energy barriers, typi-cally correlated with the activation of a protein by for example absorptionof photon energy, or binding of a ligand [108]

Almost 90% of all structures have been solved by X-ray crystallogra-phy, a technique that relies on the ability of proteins to order them-selves on a lattice of identical copies to form a crystal. Regions in theprotein that are disordered, meaning, higher fluctuations in motion,are difficult to visualize and resolve in the crystal structure. To resolvea protein to high spatial accuracy, crystallography requires extensiveaveraging to provide a measurable signal, as the signal strength is pro-portional to the crystals dimensions [116], and also square of the sumof the scattered X-rays. However, many proteins such as membraneprotein receptors and transporters are inherently flexible and crystalli-sation conditions may never be obtained. The attempt to structurallydetermine transient intermediate conformations of a protein, raiseschallenges both in regards to triggering the protein and keeping theactive conformation at large quantities in the crystalline state withoutbreaking the crystal lattice as a result of these movements [117, 118].Thus a vast majority of deposited structures are limited to the resting

3.1. Time resolved structural biology

conformation of proteins.

Going beyond the static structures of macromolecules is a tremen-dous effort. Biophysical techniques such as nuclear magnetic resonance(NMR) is a powerful tool, and is used to study protein dynamics byutilizing local fluctuating magnetic fields within a molecule [119,120].Larger proteins tumble slower, and for many years, traditional NMRwas largely limited to proteins under ca. 35 kDa [121]. However, inrecent technological developments, commercial NMR spectrometerswith 1.2 GHz field strength are available , and have extended the sizelimit of proteins and it’s sensitivity. [122] .

Absorption spectroscopy (infrared and visible) gives informationon electronic or vibrational transitions [123, 124]. Although very im-portant in understanding how a protein works, this technique is notdirectly sensitive to the structure of the protein.

MD simulations can be utilized to study equilibrium fluctuationswith high spatial resolution [125]. However, this method independentof other methods, is still too inaccurate and computationally intensiveto study protein dynamics, on time-scales beyond nano- or at best,microseconds.

Time resolved structural studies and early time resolveddiffraction studies. It has been known since the 1960’s that proteindynamics occurs in crystals [126]. Time-resolved structural studies us-ing monochromatic X-ray diffraction, utilizes intermediate trappingby controlling the rate of reaction, to accumulate a high populationof a specific intermediate. The specific conditions that affords theintermediate in high quantities can be evaluated by single crystal mi-crospectroscopy [127, 128]. The limitations of ’chemical trapping’ areseveral; ensuring intermediate is homogeneous and accumulated tohigh quantity, prolonging the life-time of an intermediate in order toperform a structural experiment is difficult, especially to short-livedintermediates, which puts the authenticity of the trapped intermediatein question [129]. Another approach to resolve transient intermediates,is by ’physical trapping’ which flash freezes the desired intermediatesfollowed by data collection at cryogenic temperatures (see reviews

Figure 3.1: Free energy landscape of a protein Resting state equi-librium fluctuations are constanstly oscillating between substates of simi-lar energy. These local flexibilities occur typically on a pico- to nanosec-ond timescale. Functionally relevant motions occur on longer timescales ofmicro- to milliseconds and requires surpassing large energy barriers, typi-cally correlated with the activation of a protein by for example absorptionof photon energy, or binding of a ligand [108]

Almost 90% of all structures have been solved by X-ray crystallogra-phy, a technique that relies on the ability of proteins to order them-selves on a lattice of identical copies to form a crystal. Regions in theprotein that are disordered, meaning, higher fluctuations in motion,are difficult to visualize and resolve in the crystal structure. To resolvea protein to high spatial accuracy, crystallography requires extensiveaveraging to provide a measurable signal, as the signal strength is pro-portional to the crystals dimensions [116], and also square of the sumof the scattered X-rays. However, many proteins such as membraneprotein receptors and transporters are inherently flexible and crystalli-sation conditions may never be obtained. The attempt to structurallydetermine transient intermediate conformations of a protein, raiseschallenges both in regards to triggering the protein and keeping theactive conformation at large quantities in the crystalline state withoutbreaking the crystal lattice as a result of these movements [117, 118].Thus a vast majority of deposited structures are limited to the resting

conformation of proteins.

Going beyond the static structures of macromolecules is a tremen-dous effort. Biophysical techniques such as nuclear magnetic resonance(NMR) is a powerful tool, and is used to study protein dynamics byutilizing local fluctuating magnetic fields within a molecule [119,120].Larger proteins tumble slower, and for many years, traditional NMRwas largely limited to proteins under ca. 35 kDa [121]. However, inrecent technological developments, commercial NMR spectrometerswith 1.2 GHz field strength are available , and have extended the sizelimit of proteins and it’s sensitivity. [122] .

Absorption spectroscopy (infrared and visible) gives informationon electronic or vibrational transitions [123, 124]. Although very im-portant in understanding how a protein works, this technique is notdirectly sensitive to the structure of the protein.

MD simulations can be utilized to study equilibrium fluctuationswith high spatial resolution [125]. However, this method independentof other methods, is still too inaccurate and computationally intensiveto study protein dynamics, on time-scales beyond nano- or at best,microseconds.

Time resolved structural studies and early time resolveddiffraction studies. It has been known since the 1960’s that proteindynamics occurs in crystals [126]. Time-resolved structural studies us-ing monochromatic X-ray diffraction, utilizes intermediate trappingby controlling the rate of reaction, to accumulate a high populationof a specific intermediate. The specific conditions that affords theintermediate in high quantities can be evaluated by single crystal mi-crospectroscopy [127, 128]. The limitations of ’chemical trapping’ areseveral; ensuring intermediate is homogeneous and accumulated tohigh quantity, prolonging the life-time of an intermediate in order toperform a structural experiment is difficult, especially to short-livedintermediates, which puts the authenticity of the trapped intermediatein question [129]. Another approach to resolve transient intermediates,is by ’physical trapping’ which flash freezes the desired intermediatesfollowed by data collection at cryogenic temperatures (see reviews

more detail [130, 131]). For light-driven proteins, photoexcitation ofcrystals at low temperatures at a given wavelength of light, have beenutilized to capture intermediates in crystals, and has been extensivelyused to elucidate intermediates during the photo-cycle of bR duringvectorial proton transport [132–142].

With Laue crystallography, the forth dimension of time was added,to record structural data in real time at room temperature, givingus the first time-resolved X-ray crystallography (TRX) experiments[143]. This was aided by advancements in optical lasers capable ofproducing femtosecond pulses for the opportunity of observing re-action dynamics on the ultra fast scale, and development of high-speed choppers to create temporal resolution in pump-probe exper-iments [144, 145]. In Laue crystallography, crystals are probed usingshort polychromatic X-ray pulses to increase the number of photons,and thus increases the X-ray flux to the crystal, and allows for a num-ber of full, rather than partial reflections to be collected without hav-ing to rotate the crystal. Several light activated systems have success-fully been characterized using time-resolved Laue diffraction includ-ing; myoglobin, at a few hundred picosecond time resolution, track-ing carbon monoxide release [146–148]. Also, the photreceptor pho-toactive yellow protein (PYP) [149, 150], and the membrane protein,photosynthetic reaction centre [151] has been resolved using time-resolved Laue diffraction, providing dynamics and structural insightswith high-resolution. However, the widespread use of this method hasbeen limited due to several limitations such as high demands on crys-tal quality and size and lower resolution in a Laue data set then thatof the corresponding monochromatic data sets [118, 129]. Measuringtransient intermediates in real time, limits X-ray exposure durationto be lower than that of the measured time point, however, shorterpulses of X-rays lower signal to noise, due to fewer photons presentto produce a full diffraction spot. The next step in the evolution oftime-resolved crystallography was the emergence of X-ray free electronlasers (XFELs).

Serial femto- and millisecond crystallography. XFELs arecapable of producing X-ray pulses that are femtoseconds (fs) in du-ration with extreme peak brilliance, delivering approximately 1012

X-ray photons per pulse which can be focused to a sub-micrometerfocal point [152] to observe femtosecond reaction dynamics. A fem-tosecond is one millionth of one billionth, of a second. To put thatin perspective, a femtosecond is to a second as a second is to about31.7 million years, and a photon of light manages to travel about0.3 µm in 1 femtosecond, and can capture the very act of atoms inmolecules moving during a chemical reaction. In comparison to thirdgeneration synchrotrons, the jump in peak brilliance is ten orders ofmagnitude. This high amount of radiation would normally annihilatethe sample, however, the ultra short exposures provides structuralinformation prior to the destroying the sample, known as "diffrac-tion before destruction", and first suggested by Neutze et al. at theturn of the 21st century [153], and later experimentally proved byChapman et.al, using a stream of nanocrystals, collecting over threemillion diffraction patterns to resolve the 3D structure of photosys-tem I [154]. XFELs offer the prospect for radiation free structuralstudies at room temperature, and as the X-ray pulse is much shorterthan that of synchtrotrons, faster time points and improved spatialresolution can be achieved [155]. These light sources have been verysuccessful in resolving time-resolved protein dynamics and intermedi-ates in photoactive yellow protein [156], myoglobin carbon monoxidedissociation [157] , bR [35,36], photosystem II [158], cytochrome C oxi-dase [159], photosynthetic reaction centre [160], bacteriorhodpsin [161]and others [162].

Serial crystallography is not only confined to XFELs, but has alsotransitioned to synchrotron light sources. Serial synchrotron crystal-lography (SSX) have been utilized as an approach to studying proteinstructural dynamics [43]. Fourth generation synchrotron light sourcesare already in use at facilities such as ESRF and MAXIV and willfurther improve the spatial and temporal resolution of future time-resolved experiments [163].

more detail [130, 131]). For light-driven proteins, photoexcitation ofcrystals at low temperatures at a given wavelength of light, have beenutilized to capture intermediates in crystals, and has been extensivelyused to elucidate intermediates during the photo-cycle of bR duringvectorial proton transport [132–142].

With Laue crystallography, the forth dimension of time was added,to record structural data in real time at room temperature, givingus the first time-resolved X-ray crystallography (TRX) experiments[143]. This was aided by advancements in optical lasers capable ofproducing femtosecond pulses for the opportunity of observing re-action dynamics on the ultra fast scale, and development of high-speed choppers to create temporal resolution in pump-probe exper-iments [144, 145]. In Laue crystallography, crystals are probed usingshort polychromatic X-ray pulses to increase the number of photons,and thus increases the X-ray flux to the crystal, and allows for a num-ber of full, rather than partial reflections to be collected without hav-ing to rotate the crystal. Several light activated systems have success-fully been characterized using time-resolved Laue diffraction includ-ing; myoglobin, at a few hundred picosecond time resolution, track-ing carbon monoxide release [146–148]. Also, the photreceptor pho-toactive yellow protein (PYP) [149, 150], and the membrane protein,photosynthetic reaction centre [151] has been resolved using time-resolved Laue diffraction, providing dynamics and structural insightswith high-resolution. However, the widespread use of this method hasbeen limited due to several limitations such as high demands on crys-tal quality and size and lower resolution in a Laue data set then thatof the corresponding monochromatic data sets [118, 129]. Measuringtransient intermediates in real time, limits X-ray exposure durationto be lower than that of the measured time point, however, shorterpulses of X-rays lower signal to noise, due to fewer photons presentto produce a full diffraction spot. The next step in the evolution oftime-resolved crystallography was the emergence of X-ray free electronlasers (XFELs).

Serial femto- and millisecond crystallography. XFELs arecapable of producing X-ray pulses that are femtoseconds (fs) in du-ration with extreme peak brilliance, delivering approximately 1012

X-ray photons per pulse which can be focused to a sub-micrometerfocal point [152] to observe femtosecond reaction dynamics. A fem-tosecond is one millionth of one billionth, of a second. To put thatin perspective, a femtosecond is to a second as a second is to about31.7 million years, and a photon of light manages to travel about0.3 µm in 1 femtosecond, and can capture the very act of atoms inmolecules moving during a chemical reaction. In comparison to thirdgeneration synchrotrons, the jump in peak brilliance is ten orders ofmagnitude. This high amount of radiation would normally annihilatethe sample, however, the ultra short exposures provides structuralinformation prior to the destroying the sample, known as "diffrac-tion before destruction", and first suggested by Neutze et al. at theturn of the 21st century [153], and later experimentally proved byChapman et.al, using a stream of nanocrystals, collecting over threemillion diffraction patterns to resolve the 3D structure of photosys-tem I [154]. XFELs offer the prospect for radiation free structuralstudies at room temperature, and as the X-ray pulse is much shorterthan that of synchtrotrons, faster time points and improved spatialresolution can be achieved [155]. These light sources have been verysuccessful in resolving time-resolved protein dynamics and intermedi-ates in photoactive yellow protein [156], myoglobin carbon monoxidedissociation [157] , bR [35,36], photosystem II [158], cytochrome C oxi-dase [159], photosynthetic reaction centre [160], bacteriorhodpsin [161]and others [162].

Serial crystallography is not only confined to XFELs, but has alsotransitioned to synchrotron light sources. Serial synchrotron crystal-lography (SSX) have been utilized as an approach to studying proteinstructural dynamics [43]. Fourth generation synchrotron light sourcesare already in use at facilities such as ESRF and MAXIV and willfurther improve the spatial and temporal resolution of future time-resolved experiments [163].

Serial crystallography offers unique and rich insights into biology,especially to observe ultra fast dynamics. However, crystals are bytheir very nature likely to hinder larger conformational changes on atertiary and quaternary scale, as the protein is confined within thecrystal lattice. X-ray diffraction methods applicable to proteins in so-lution can principally avoid many of the limitations of crystal basedapproaches. Studying protein dynamics in solution allows capturingprotein reactions in a more natural environment, near physiologicalconditions. Also, solution scattering methods are great for irreversiblereactions, otherwise difficult using crystallography, by providing freshsample via a flow-cell for each instance of initiating the reaction [164].In stark contrast to crystallography, proteins in solution are com-pletely disordered which comes at the expense of lower structural res-olution. However, the further development in for example softwaretools in conjunction with molecular dynamics simulations, for anal-ysis and modeling, makes time-resolved X-ray solution scattering ahighly complementary method to crystallography.

Figure 3.2: Schematic illustration of time-resolved X-ray solutionscattering experiment of small molecules in solution Directly copiedfrom Neutze et al. [165] with reuse and permission licence from APS.

Solution scattering at small angles (SAXS). Small angleX-ray solution scattering (SAXS) measurements, can elucidate theoverall size and shape of the protein by taking advantage of the infor-mation content available at small angles, corresponding to spacings of∼15-20 Å (q∼0.3 Å−1) [166] to resolve the molecular envelope on thequaternary and tertiary scale. [167–169]. In contrast to crystallogra-phy, there is less demand on the sample, and data can be collected onrelatively small protein samples. Also, SAXS is less hindered by parti-cle size and characterization of large multimeric systems and proteincomplexes is achievable. Advancements in sample delivery and datacollection strategies have automated much of the experimental proce-dure, sample characterization and time-resolved data collection [169].The largest challenges resides in retrieving three dimensional struc-tural information from one- dimensional SAXS scattering profiles.Software tools play a vital part of structural analysis and model-ing, and publicly available software tools dedicated to SAXS analysissuch as restoring low-resolution structures using ab initio shape deter-mination [170, 171] and modeling from atomic coordinates of crystalstructures [172,173].

Time-resolved X-ray solution scattering Utilizing much ofthe same methodology as SAXS, Wide angle X-ray scattering (WAXS)samples information at higher angles, as a result of shorter sample-to-detector distance. The time-resolved WAXS experiments (TR-WAXS)methodology originates from earlier studies of small photo-chemicalreactions in solution (see fig 3.2) [150,165,174–176], and then extendedto proteins by Cammarata et al [177], using time resolved X-ray so-lution scattering (TR-XSS) to study the ultrafast dynamics of photo-disassociation of carbon monoxide from tetrameric haemoglobin andcytochrome c. In contrast to SAXS, TR-XSS extends scattering an-gles to q ∼ 2.5 Å−1, although most structural information is usuallybetween q = 0.15 < q < 1.0 Å−1 [178] as the solvent contributionis increasingly intense at scattering angles beyond q ∼ 1 Å−1 [166].Different regions of q space correlate to approximate distances in realspace, such that tertiary structural movements correspond approxi-mately to 0.15 < q < 0.25 Å−1, secondary structures, like helices and

Serial crystallography offers unique and rich insights into biology,especially to observe ultra fast dynamics. However, crystals are bytheir very nature likely to hinder larger conformational changes on atertiary and quaternary scale, as the protein is confined within thecrystal lattice. X-ray diffraction methods applicable to proteins in so-lution can principally avoid many of the limitations of crystal basedapproaches. Studying protein dynamics in solution allows capturingprotein reactions in a more natural environment, near physiologicalconditions. Also, solution scattering methods are great for irreversiblereactions, otherwise difficult using crystallography, by providing freshsample via a flow-cell for each instance of initiating the reaction [164].In stark contrast to crystallography, proteins in solution are com-pletely disordered which comes at the expense of lower structural res-olution. However, the further development in for example softwaretools in conjunction with molecular dynamics simulations, for anal-ysis and modeling, makes time-resolved X-ray solution scattering ahighly complementary method to crystallography.

Figure 3.2: Schematic illustration of time-resolved X-ray solutionscattering experiment of small molecules in solution Directly copiedfrom Neutze et al. [165] with reuse and permission licence from APS.

Solution scattering at small angles (SAXS). Small angleX-ray solution scattering (SAXS) measurements, can elucidate theoverall size and shape of the protein by taking advantage of the infor-mation content available at small angles, corresponding to spacings of∼15-20 Å (q∼0.3 Å−1) [166] to resolve the molecular envelope on thequaternary and tertiary scale. [167–169]. In contrast to crystallogra-phy, there is less demand on the sample, and data can be collected onrelatively small protein samples. Also, SAXS is less hindered by parti-cle size and characterization of large multimeric systems and proteincomplexes is achievable. Advancements in sample delivery and datacollection strategies have automated much of the experimental proce-dure, sample characterization and time-resolved data collection [169].The largest challenges resides in retrieving three dimensional struc-tural information from one- dimensional SAXS scattering profiles.Software tools play a vital part of structural analysis and model-ing, and publicly available software tools dedicated to SAXS analysissuch as restoring low-resolution structures using ab initio shape deter-mination [170, 171] and modeling from atomic coordinates of crystalstructures [172,173].

Time-resolved X-ray solution scattering Utilizing much ofthe same methodology as SAXS, Wide angle X-ray scattering (WAXS)samples information at higher angles, as a result of shorter sample-to-detector distance. The time-resolved WAXS experiments (TR-WAXS)methodology originates from earlier studies of small photo-chemicalreactions in solution (see fig 3.2) [150,165,174–176], and then extendedto proteins by Cammarata et al [177], using time resolved X-ray so-lution scattering (TR-XSS) to study the ultrafast dynamics of photo-disassociation of carbon monoxide from tetrameric haemoglobin andcytochrome c. In contrast to SAXS, TR-XSS extends scattering an-gles to q ∼ 2.5 Å−1, although most structural information is usuallybetween q = 0.15 < q < 1.0 Å−1 [178] as the solvent contributionis increasingly intense at scattering angles beyond q ∼ 1 Å−1 [166].Different regions of q space correlate to approximate distances in realspace, such that tertiary structural movements correspond approxi-mately to 0.15 < q < 0.25 Å−1, secondary structures, like helices and

Figure 3.3: WAXS structural information Approximate correlationbetween q-space and real space, where where q = 4π sin(θ)/λ

sheets (0.25 < q < 0.6 Å−1), and between atoms which makes up thesecondary structures (q > 0.6 Å−1) (see 3.3) [177].

Many other light-triggered reactions have been studied using time-resolved X-ray solution scattering (TR-XSS) (general experimentalsetup fig 3.4) including CO photo-disassociation from haem groups ofmyoglobin [177, 179–181, 181], and chromophore isomerization-drivenreactions within photoactive yellow protein [182,183], bacteriorhodopsin[178], proteorhodopsin [184, 185], SRII / SRII-HtrII (PAPER II),and ChR2 (PAPER III). TR-XSS has also helped elucidate pho-toactivation, signal amplification and transduction of bacterial phy-tochromes [186,187] and structural activation of light-oxygen-voltage(LOV) photoreceptor [188].

TR-XSS extension to XFEL’s was suggested initially [152] andwas shortly after implemented experimentally to observe ultra-faststructural changes in microbial photosynthetic reaction centre [189],ultrafast myoglobin structural dynamics [190] and coherent diffractiveimaging of microtubules [191].

Figure 3.4: Schematic illustration showing typical experimentalsetup for time-resolved X-ray solution scattering (TR-XSS) ofproteins Image directly copied from Ihee et al [176] with permission frompublishers Taylor & Francis.

Like SAXS, TR-XSS is limited by data interpretation and much ofthe development in this field is focused on the structural analysis andmodeling from low resolution data, such as rigid body modeling [76],or experimentally guided MD simulations [192].

TR-XSS lies at the intersect between X-ray crystallography andabsorption spectroscopy. The method is directly sensitive to the threedimensional structure of the protein. By utilizing the high resolutionstructures from time resolved serial crystallography experiments, andMD simulations, TR-XSS enables the direct study of protein struc-tural dynamics, in solution, in real time.

Figure 3.3: WAXS structural information Approximate correlationbetween q-space and real space, where where q = 4π sin(θ)/λ

sheets (0.25 < q < 0.6 Å−1), and between atoms which makes up thesecondary structures (q > 0.6 Å−1) (see 3.3) [177].

Many other light-triggered reactions have been studied using time-resolved X-ray solution scattering (TR-XSS) (general experimentalsetup fig 3.4) including CO photo-disassociation from haem groups ofmyoglobin [177, 179–181, 181], and chromophore isomerization-drivenreactions within photoactive yellow protein [182,183], bacteriorhodopsin[178], proteorhodopsin [184, 185], SRII / SRII-HtrII (PAPER II),and ChR2 (PAPER III). TR-XSS has also helped elucidate pho-toactivation, signal amplification and transduction of bacterial phy-tochromes [186,187] and structural activation of light-oxygen-voltage(LOV) photoreceptor [188].

TR-XSS extension to XFEL’s was suggested initially [152] andwas shortly after implemented experimentally to observe ultra-faststructural changes in microbial photosynthetic reaction centre [189],ultrafast myoglobin structural dynamics [190] and coherent diffractiveimaging of microtubules [191].

Figure 3.4: Schematic illustration showing typical experimentalsetup for time-resolved X-ray solution scattering (TR-XSS) ofproteins Image directly copied from Ihee et al [176] with permission frompublishers Taylor & Francis.

Like SAXS, TR-XSS is limited by data interpretation and much ofthe development in this field is focused on the structural analysis andmodeling from low resolution data, such as rigid body modeling [76],or experimentally guided MD simulations [192].

TR-XSS lies at the intersect between X-ray crystallography andabsorption spectroscopy. The method is directly sensitive to the threedimensional structure of the protein. By utilizing the high resolutionstructures from time resolved serial crystallography experiments, andMD simulations, TR-XSS enables the direct study of protein struc-tural dynamics, in solution, in real time.

Chapter 4

X-ray scattering

In 1895 whilst experimenting with electric current flow in partial vac-uum tubes, Röntgen accidentally discovered an unknown radiationwhich he later named X-rays [193]. In 1912 Laue and coworkers madethe first observations of X-ray crystal diffraction [194, 195] and thein the following year, Bragg, father and son, solved the first crystalstructures of NaCl and KCl [196,197].

In order for an object to diffract light, the wavelength (λ) should beapproximately no larger than the object we wish to resolve. Individualbonded atoms in protein molecules have a distance around 0.1 nm (1Å), and electromagnetic radiation of this wavelength corresponds tothe range of X-rays. X-rays have an wavelength in the range of 0.1 Å- 100 Å, which corresponds to photon energies ranging from 124 eV to124 KeV. The wavelength of X-rays can therefore be diffracted by eventhe smallest molecules [198], although X-ray diffraction experimentstypically use a wavelength about 1 Å (10−10 m), corresponding tothe interatomic distances. Therefore, X-ray radiation scattered froma given sample, yields information on the internal atomic or molecularstructure, and is frequently utilized for structural studies in physics,chemistry and biology.

This chapter, section 4.1, will introduce the basic principles of X-ray scattering by an atom, by a group of electrons and scattering byproteins in solution.

Chapter 4

X-ray scattering

In 1895 whilst experimenting with electric current flow in partial vac-uum tubes, Röntgen accidentally discovered an unknown radiationwhich he later named X-rays [193]. In 1912 Laue and coworkers madethe first observations of X-ray crystal diffraction [194, 195] and thein the following year, Bragg, father and son, solved the first crystalstructures of NaCl and KCl [196,197].

In order for an object to diffract light, the wavelength (λ) should beapproximately no larger than the object we wish to resolve. Individualbonded atoms in protein molecules have a distance around 0.1 nm (1Å), and electromagnetic radiation of this wavelength corresponds tothe range of X-rays. X-rays have an wavelength in the range of 0.1 Å- 100 Å, which corresponds to photon energies ranging from 124 eV to124 KeV. The wavelength of X-rays can therefore be diffracted by eventhe smallest molecules [198], although X-ray diffraction experimentstypically use a wavelength about 1 Å (10−10 m), corresponding tothe interatomic distances. Therefore, X-ray radiation scattered froma given sample, yields information on the internal atomic or molecularstructure, and is frequently utilized for structural studies in physics,chemistry and biology.

This chapter, section 4.1, will introduce the basic principles of X-ray scattering by an atom, by a group of electrons and scattering byproteins in solution.

Chapter 4. X-ray scattering

4.1 FundamentalsScattering by an atom X-rays are scattered by the electrons ofatoms. Scattering can be described as oscillating electromagnetic fieldsof an incident X-ray beam, interacting with the electron cloud of theatom. This electric field exerts forces on the interacting electrons ofthe atom, causing them to accelerate. The acceleration of the electroncauses it to emit radiation, spreading out in all directions from theatom with the same wavelength as the incident X-ray, in a processknown as elastic scattering or Thomson scattering. Inelastic X-rayscattering also known as Compton scattering and absorption of theX-ray photon can also occur, however, this will not be covered in thisthesis. The combined scattering from all electrons, produces a spher-ical wave emanating from the atom, and even though each electronscatters X-rays with the same amplitude, the size and distributionof the electron cloud affects the amplitude of the X-rays scatteredby the atom as a function of the scattering angle. The scattering de-creases with increasing angles, and X-ray scattering power, increaseswith the size of the atoms. The relationship between the size or shapeof the atom and its angular distribution of scattering power is com-monly described by the form factor (f), and is characteristic for eachelement [193]. Light elements contain fewer electrons than that ofheavier elements, and as such have lower scattering amplitudes and astrong angular dependence. When scattering originates from differentatoms in a molecule, the scattering waves add and interfere. Lookingat figure 4.1, the observed wave at the point of observation P , is thesum of the scattered waves from particles A and B. The wave of A andB will reach P with different phases, as they have travelled a differ-ent distance from the plane front W to the point of observation. Thephase shift is dependent on the difference �|WA|+ �|AP |− �|WB|+ �|BP |.

Scattering by a group of electrons Calculating scattering am-plitude (F ) of any number of electrons, from an atom or a molecule,the sum of all waves originating from each electron and the shiftin phase as a result of the separation between electrons is consid-ered [199]. In figure 4.2, the detector distance is much greater than

4.1. Fundamentals

Figure 4.1: Scattering from two points

that of the distances in the sample. Elastic scattering can be describedhere as the incident and scattered wave vectors which are representedby �k0 and �k respectively. The incident plane wave �k0, with | �k0| = 2π

λ(where lambda represents the wavelength), is scattered with the vector�k. The difference between the incident and scattering vector defines⇒ q (q = |�k− �k0| = 4π

λ sin(θ)), which is the momentum transfer, where2θ is the scattering angle. Equation 4.1 describes the sum of scatteredwaves for N electrons at distance rn around an atom centered at O,as shown in figure 4.2, and fe is the scattering factor per electron.

Figure 4.2: Scattering by a general sample

F (q) =N∑

n=1fe exp iq · rn (4.1)

4.1 FundamentalsScattering by an atom X-rays are scattered by the electrons ofatoms. Scattering can be described as oscillating electromagnetic fieldsof an incident X-ray beam, interacting with the electron cloud of theatom. This electric field exerts forces on the interacting electrons ofthe atom, causing them to accelerate. The acceleration of the electroncauses it to emit radiation, spreading out in all directions from theatom with the same wavelength as the incident X-ray, in a processknown as elastic scattering or Thomson scattering. Inelastic X-rayscattering also known as Compton scattering and absorption of theX-ray photon can also occur, however, this will not be covered in thisthesis. The combined scattering from all electrons, produces a spher-ical wave emanating from the atom, and even though each electronscatters X-rays with the same amplitude, the size and distributionof the electron cloud affects the amplitude of the X-rays scatteredby the atom as a function of the scattering angle. The scattering de-creases with increasing angles, and X-ray scattering power, increaseswith the size of the atoms. The relationship between the size or shapeof the atom and its angular distribution of scattering power is com-monly described by the form factor (f), and is characteristic for eachelement [193]. Light elements contain fewer electrons than that ofheavier elements, and as such have lower scattering amplitudes and astrong angular dependence. When scattering originates from differentatoms in a molecule, the scattering waves add and interfere. Lookingat figure 4.1, the observed wave at the point of observation P , is thesum of the scattered waves from particles A and B. The wave of A andB will reach P with different phases, as they have travelled a differ-ent distance from the plane front W to the point of observation. Thephase shift is dependent on the difference �|WA|+ �|AP |− �|WB|+ �|BP |.

Scattering by a group of electrons Calculating scattering am-plitude (F ) of any number of electrons, from an atom or a molecule,the sum of all waves originating from each electron and the shiftin phase as a result of the separation between electrons is consid-ered [199]. In figure 4.2, the detector distance is much greater than

4.1. Fundamentals

Figure 4.1: Scattering from two points

that of the distances in the sample. Elastic scattering can be describedhere as the incident and scattered wave vectors which are representedby �k0 and �k respectively. The incident plane wave �k0, with | �k0| = 2π

λ(where lambda represents the wavelength), is scattered with the vector�k. The difference between the incident and scattering vector defines⇒ q (q = |�k− �k0| = 4π

λ sin(θ)), which is the momentum transfer, where2θ is the scattering angle. Equation 4.1 describes the sum of scatteredwaves for N electrons at distance rn around an atom centered at O,as shown in figure 4.2, and fe is the scattering factor per electron.

Figure 4.2: Scattering by a general sample

F (q) =N∑

n=1fe exp iq · rn (4.1)

Solution scattering As mentioned earlier, X-rays are scatteredby the electron cloud of the atom, and the scattering amplitude in-creases with the atomic number, as there are more electrons. Theatomic scattering factor f(q) can be calculated for a given atom, whichis a measure of the scattering amplitude. For atoms where the electrondensity can be represented as spherically symmetric P (r), the formfactor can be described as in equation 4.2.

f(q) = 4π

∫ρ(r)r2 sin(qr)

qrdr (4.2)

And so to calculate the scattering amplitude for a group of atomslocated at rn, the scatter factor per electron as seen in equation 4.1 canbe substituted for atomic scattering factor in equation 4.2. Pairwisedistance distribution function is a way to define electron distancesin the a given sample where identical distances are added together.P (r) is defined in real space and an inverse Fourier transform givesus the scattering curve intensity in reciprocal space q affording usequation 4.2. Likewise the spatial density distribution P (r) can beretrieved by Fourier inversion of the scattering curve [200], providingdirect information of the distances between electron of the scatteringparticles in a given sample [201].

In an solution scattering experiment, with a sample containingproteins, the scattered amplitude is not measured, but rather the in-tensity. The intensity of the scattered X-ray beam is proportional tothe magnitude squared of the X-ray scattering.

I(q) = |F (q)|2 (4.3)

Furthermore, for solution scattering experiments, the content ofthe sample measured consists of a large number of identical proteinmolecules, randomly and uniformly oriented, as can be seen in fig-ure 4.3. Because the proteins in solution are completely disordered,all possible orientations are measured simultaneously [202], and thedetector image shows isotropic scattering. The spherically symmetricdetector image can be radially integrated into a 1D scattering curve.

4.1. Fundamentals

The magnitude of the scattering vector is represented by

q = 4π

sin(θ) (4.4)

Figure 4.3: Scattering by proteins in solution

The one dimensional intensity is yielded by calculating sphericalaverage, by assuming that all atoms scatter spherically, given by theDebye equation 4.5

I(q) =∑m

fm(q)fn(q)sin(qrmn)qrmn

where fm and fn are the atomic scattering factors and rmn =|rm − rn|, rmn is the distance between atom m and n

For solution scattering, we can define a relationship between realspace r and reciprocal space q with equation 4.6

r = 2π

q(4.6)

Proteins completely disordered in solution do not have transla-tional symmetry like proteins in crystals. Due to solution average onlyinteratomic distances are measured, not atomic coordinates. The De-bye equation states that each scattering pair, or each atomic distance

Solution scattering As mentioned earlier, X-rays are scatteredby the electron cloud of the atom, and the scattering amplitude in-creases with the atomic number, as there are more electrons. Theatomic scattering factor f(q) can be calculated for a given atom, whichis a measure of the scattering amplitude. For atoms where the electrondensity can be represented as spherically symmetric P (r), the formfactor can be described as in equation 4.2.

f(q) = 4π

∫ρ(r)r2 sin(qr)

qrdr (4.2)

And so to calculate the scattering amplitude for a group of atomslocated at rn, the scatter factor per electron as seen in equation 4.1 canbe substituted for atomic scattering factor in equation 4.2. Pairwisedistance distribution function is a way to define electron distancesin the a given sample where identical distances are added together.P (r) is defined in real space and an inverse Fourier transform givesus the scattering curve intensity in reciprocal space q affording usequation 4.2. Likewise the spatial density distribution P (r) can beretrieved by Fourier inversion of the scattering curve [200], providingdirect information of the distances between electron of the scatteringparticles in a given sample [201].

In an solution scattering experiment, with a sample containingproteins, the scattered amplitude is not measured, but rather the in-tensity. The intensity of the scattered X-ray beam is proportional tothe magnitude squared of the X-ray scattering.

I(q) = |F (q)|2 (4.3)

Furthermore, for solution scattering experiments, the content ofthe sample measured consists of a large number of identical proteinmolecules, randomly and uniformly oriented, as can be seen in fig-ure 4.3. Because the proteins in solution are completely disordered,all possible orientations are measured simultaneously [202], and thedetector image shows isotropic scattering. The spherically symmetricdetector image can be radially integrated into a 1D scattering curve.

4.1. Fundamentals

The magnitude of the scattering vector is represented by

q = 4π

sin(θ) (4.4)

Figure 4.3: Scattering by proteins in solution

The one dimensional intensity is yielded by calculating sphericalaverage, by assuming that all atoms scatter spherically, given by theDebye equation 4.5

I(q) =∑m

fm(q)fn(q)sin(qrmn)qrmn

where fm and fn are the atomic scattering factors and rmn =|rm − rn|, rmn is the distance between atom m and n

For solution scattering, we can define a relationship between realspace r and reciprocal space q with equation 4.6

r = 2π

q(4.6)

Proteins completely disordered in solution do not have transla-tional symmetry like proteins in crystals. Due to solution average onlyinteratomic distances are measured, not atomic coordinates. The De-bye equation states that each scattering pair, or each atomic distance

rmn in the molecule adds a sin(qr)/(qr) to the scattering intensity,and gives a positive contribution for

q < 2π/r (4.7)

Smaller distances will thus contribute to scattering at higher an-gles, whereas longer distances correspond to scattering contributionat smaller angles. It is thus reasonable that equation 4.6 can approx-imate the relationship between reciprocal (q) and real space (r) fromscattering proteins in solution.

When collecting data at an X-ray solution scattering (XSS) ex-periment, the protein is suspended in a buffer solution, and we mea-sure scattering from the entire sample including the macromoleculesand the solvent scattering background. In order to create contrast,meaning that which makes the particles of interest "visible", one mustconsider the not only the scattered waves of the protein, but also thewaves from the scattered solvent which would of been there, if the pro-tein had not taken up that volume. This is know as excluded volumeand is illustratively described in figure 4.4

Figure 4.4: Background scattering and X-ray contrast. Schematicillustration of an object scattering in solution. The scattering contributionsinclude, (i) The object in solution (ii) the object of interest in vacuo, (iii)the bulk solvent and (iiii) the excluded volume

From figure 4.4 we can describe the different scattering contribu-tions,

(ii) = F (q) =∑

fm exp(iq · rm) (4.8)

4.1. Fundamentals

(iiii) =∑m

ρ◦vm exp(iq · rm) (4.9)

In equation 4.9 ρ◦ is the solvent scattering density of an infinitehomogeneous solvent. So to describe the scattering amplitude of aparticle in solution (i) from figure 4.4 is given by

(i) = F (q) =∑m

(fm − ρ◦vm) exp(iq · rm) (4.10)

Where vm is the volume of atom m and the fm − ρ◦vm term rep-resents the contrast amplitude for atom m in the protein in relationto the solvent.

If we take into account the terms for contrast, then we can re-writethe Debye equation to what is experimentally measured, which is theintensity relating to the atomic scattering factor with a given angle q.

I(q) = 〈(∑m

(fm − ρ◦vm)(fn − ρ◦vn)sin(qrmn)qrmn

)〉 (4.11)

The 〈...〉 in equation 4.11 represents the average over all orienta-tions.

Another factor is the hydration layer, which is the solvent closest tothe proteins surface, and has therefore a slightly different density fromthat of the bulk solvent. These contributing factors can be summarisedas such

I(q) = 〈(Fvac. − Fexcl. + Fhyd.)(Fvac. − Fexcl. + Fhyd.)∗〉 (4.12)

Where the terms inside the parenthesis of equation 4.12 representsscattering amplitude or intensity of the protein in vacuum (Fvac.),from the excluded solvent (F excl.) and the hydration layer (Fhyd.).

rmn in the molecule adds a sin(qr)/(qr) to the scattering intensity,and gives a positive contribution for

q < 2π/r (4.7)

Smaller distances will thus contribute to scattering at higher an-gles, whereas longer distances correspond to scattering contributionat smaller angles. It is thus reasonable that equation 4.6 can approx-imate the relationship between reciprocal (q) and real space (r) fromscattering proteins in solution.

When collecting data at an X-ray solution scattering (XSS) ex-periment, the protein is suspended in a buffer solution, and we mea-sure scattering from the entire sample including the macromoleculesand the solvent scattering background. In order to create contrast,meaning that which makes the particles of interest "visible", one mustconsider the not only the scattered waves of the protein, but also thewaves from the scattered solvent which would of been there, if the pro-tein had not taken up that volume. This is know as excluded volumeand is illustratively described in figure 4.4

Figure 4.4: Background scattering and X-ray contrast. Schematicillustration of an object scattering in solution. The scattering contributionsinclude, (i) The object in solution (ii) the object of interest in vacuo, (iii)the bulk solvent and (iiii) the excluded volume

From figure 4.4 we can describe the different scattering contribu-tions,

(ii) = F (q) =∑

fm exp(iq · rm) (4.8)

4.1. Fundamentals

(iiii) =∑m

ρ◦vm exp(iq · rm) (4.9)

In equation 4.9 ρ◦ is the solvent scattering density of an infinitehomogeneous solvent. So to describe the scattering amplitude of aparticle in solution (i) from figure 4.4 is given by

(i) = F (q) =∑m

(fm − ρ◦vm) exp(iq · rm) (4.10)

Where vm is the volume of atom m and the fm − ρ◦vm term rep-resents the contrast amplitude for atom m in the protein in relationto the solvent.

If we take into account the terms for contrast, then we can re-writethe Debye equation to what is experimentally measured, which is theintensity relating to the atomic scattering factor with a given angle q.

I(q) = 〈(∑m

(fm − ρ◦vm)(fn − ρ◦vn)sin(qrmn)qrmn

)〉 (4.11)

The 〈...〉 in equation 4.11 represents the average over all orienta-tions.

Another factor is the hydration layer, which is the solvent closest tothe proteins surface, and has therefore a slightly different density fromthat of the bulk solvent. These contributing factors can be summarisedas such

I(q) = 〈(Fvac. − Fexcl. + Fhyd.)(Fvac. − Fexcl. + Fhyd.)∗〉 (4.12)

Where the terms inside the parenthesis of equation 4.12 representsscattering amplitude or intensity of the protein in vacuum (Fvac.),from the excluded solvent (F excl.) and the hydration layer (Fhyd.).

Chapter 5

Time-resolved X-raysolution scatteringmethodology

5.1 Generating intense X-rays-synchrotron lightsource

The first generation synchrotrons were made in the 1950 to studyhigh-energy particle physics, where the by-product was synchrotronradiation. In the 1980s, United Kingdom constructed the first second-generation synchrotron radiation source, specifically designed to pro-duce high intensity radiation for research [203]. Third-generation syn-chrotron were optimized for brightness, and the European SynchrotronRadiation Facility (ESRF) was the first to operate in 1994 [204]. In thepresent moment, fourth generation synchrotrons are being put to useat facilities such as ESRF and MAX IV with an increase in brightnessby a factor of 100 from it’s predecessor [205,206].

In synchrotrons, electromagnetic radiation is emitted by an accel-erated charge moving at relativistic speed. The underlying physicalphenomenon that drives synchrotron science is that a moving elec-tron that changes direction, is attenuated, emits energy, and if it’smoving fast enough, the emitted energy is at a wavelength that cor-responds to X-rays. First, the electrons are produced by a electrongun in the linear accelerator (LINAC), where the electrons are packed

Chapter 5

Time-resolved X-raysolution scatteringmethodology

5.1 Generating intense X-rays-synchrotron lightsource

The first generation synchrotrons were made in the 1950 to studyhigh-energy particle physics, where the by-product was synchrotronradiation. In the 1980s, United Kingdom constructed the first second-generation synchrotron radiation source, specifically designed to pro-duce high intensity radiation for research [203]. Third-generation syn-chrotron were optimized for brightness, and the European SynchrotronRadiation Facility (ESRF) was the first to operate in 1994 [204]. In thepresent moment, fourth generation synchrotrons are being put to useat facilities such as ESRF and MAX IV with an increase in brightnessby a factor of 100 from it’s predecessor [205,206].

In synchrotrons, electromagnetic radiation is emitted by an accel-erated charge moving at relativistic speed. The underlying physicalphenomenon that drives synchrotron science is that a moving elec-tron that changes direction, is attenuated, emits energy, and if it’smoving fast enough, the emitted energy is at a wavelength that cor-responds to X-rays. First, the electrons are produced by a electrongun in the linear accelerator (LINAC), where the electrons are packed

Chapter 5. Time-resolved X-ray solution scattering methodology

into "bunches" and accelerated, to increase their energy to 200-300million electron-volts, and then injected into the booster ring. Thebooster ring accelerates the electrons to reach an energy of 6 billionelectron-volts (6GeV) before being injected into the storage ring. Thestorage ring is several hundred meters in circumference, and the elec-trons circle near the speed of light. There are three main type ofmagnets which the electrons pass through as they travel around thering. The storage ring has alternating curved and straight sections.In the curved section, there are bending magnets where strong mag-netic fields perpendicular to the beam are applied to the electrons,forcing their path into their circular orbit around the ring. In thestraight sections there are focusing magnets that enforce the electronsto stay as close as possible to their ideal orbital path and there arealso undulators which are made up of a complex arrangement of smallmagnets, forcing the electrons to follow a wave-like trajectory. Themagnets cause a traverse acceleration of the relativistic electrons ateach bend, and radiation is emitted from each consecutive bend alongthe undulator, which overlaps and interferes, and so the accumulatedinterference of emitted radiation across the trajectory of the undula-tor generates a very focused and brilliant beam. The gaps between theundulator magnets can be altered in order to fine-tune the wavelengthof the X-rays in the beam.

The time-resolved X-ray solution scattering data in this thesiswas collected at the ESRF at beamline ID09 in Grenoble, France,on a third-generation synhrotron, with the last data set collected onemonth prior to the 20-month shutdown for the upgrade and comple-tion of ESRF-Extremely Brilliant Source (ESRF-EBS).

5.2 Experimental setupDetecting the signal. To capture structural changes in a proteinin real time, the protein must be perturbed from its resting state,and the following reaction monitored over time. The time-resolutionis often limited by how fast data can be detected and stored, defined

5.2. Experimental setup

by the integration and read-out time of the detector. In rapid readout mode, the detector act much like a video camera, recording aseries of frames, which limits the time-resolution for studying reactionson scales from milliseconds to seconds [207, p.24]. Data acquisitionstrategy using monochromatic X-rays to capture a time-dependantTR-XSS of proteorhodopsin has been carried out by using a rapid-readdetector by Westenhoff et al. with a time-resolution of 10ms [208].

Figure 5.1: Pump-probe

An alternative to this detection scheme is pump-probe technique.For light activated proteins, the resting state is activated via a laserpulse (pump), and the wavelength of that laser pulse depends on theabsorption wavelength of a given chromophore. As the protein evolvesthrough it’s transient intermediates, a snapshot is captured using X-rays (probe). This mode is implemented at the ID09 beamline atESRF, and uses polychromatic (pink) beams, that are produced inthe aforementioned undulators [209], and runs through an optical sec-tion that consists of a heat load chopper, a millisecond shutter and arotating chopper, that can select single X-ray pulses from the storagering by rotating at a frequency of 986.3 Hz [210]. The difference intime between the laser pulse and the X-ray pulse, defines the time-point (figure 5.1), and by selecting a range of time-delays betweenpump and probe, the reaction process can be sampled in real timeover several orders of magnitude. By repeatedly measuring over sev-eral delays in cycles, the scattered photons can be recorded on anintegrating CCD detector, since each time delay in the series repre-sents the same dynamic state. This detector only needs to be read out

into "bunches" and accelerated, to increase their energy to 200-300million electron-volts, and then injected into the booster ring. Thebooster ring accelerates the electrons to reach an energy of 6 billionelectron-volts (6GeV) before being injected into the storage ring. Thestorage ring is several hundred meters in circumference, and the elec-trons circle near the speed of light. There are three main type ofmagnets which the electrons pass through as they travel around thering. The storage ring has alternating curved and straight sections.In the curved section, there are bending magnets where strong mag-netic fields perpendicular to the beam are applied to the electrons,forcing their path into their circular orbit around the ring. In thestraight sections there are focusing magnets that enforce the electronsto stay as close as possible to their ideal orbital path and there arealso undulators which are made up of a complex arrangement of smallmagnets, forcing the electrons to follow a wave-like trajectory. Themagnets cause a traverse acceleration of the relativistic electrons ateach bend, and radiation is emitted from each consecutive bend alongthe undulator, which overlaps and interferes, and so the accumulatedinterference of emitted radiation across the trajectory of the undula-tor generates a very focused and brilliant beam. The gaps between theundulator magnets can be altered in order to fine-tune the wavelengthof the X-rays in the beam.

The time-resolved X-ray solution scattering data in this thesiswas collected at the ESRF at beamline ID09 in Grenoble, France,on a third-generation synhrotron, with the last data set collected onemonth prior to the 20-month shutdown for the upgrade and comple-tion of ESRF-Extremely Brilliant Source (ESRF-EBS).

5.2 Experimental setupDetecting the signal. To capture structural changes in a proteinin real time, the protein must be perturbed from its resting state,and the following reaction monitored over time. The time-resolutionis often limited by how fast data can be detected and stored, defined

5.2. Experimental setup

by the integration and read-out time of the detector. In rapid readout mode, the detector act much like a video camera, recording aseries of frames, which limits the time-resolution for studying reactionson scales from milliseconds to seconds [207, p.24]. Data acquisitionstrategy using monochromatic X-rays to capture a time-dependantTR-XSS of proteorhodopsin has been carried out by using a rapid-readdetector by Westenhoff et al. with a time-resolution of 10ms [208].

Figure 5.1: Pump-probe

An alternative to this detection scheme is pump-probe technique.For light activated proteins, the resting state is activated via a laserpulse (pump), and the wavelength of that laser pulse depends on theabsorption wavelength of a given chromophore. As the protein evolvesthrough it’s transient intermediates, a snapshot is captured using X-rays (probe). This mode is implemented at the ID09 beamline atESRF, and uses polychromatic (pink) beams, that are produced inthe aforementioned undulators [209], and runs through an optical sec-tion that consists of a heat load chopper, a millisecond shutter and arotating chopper, that can select single X-ray pulses from the storagering by rotating at a frequency of 986.3 Hz [210]. The difference intime between the laser pulse and the X-ray pulse, defines the time-point (figure 5.1), and by selecting a range of time-delays betweenpump and probe, the reaction process can be sampled in real timeover several orders of magnitude. By repeatedly measuring over sev-eral delays in cycles, the scattered photons can be recorded on anintegrating CCD detector, since each time delay in the series repre-sents the same dynamic state. This detector only needs to be read out

once it reaches the limit of it’s dynamic range. The time-resolutionhere is defined by the length of a single X-ray pulse, which is 100 ps,and longer time-points can be achieved by varying the opening timesof the chopper, so it can transmit more than one X-ray pulse. The de-lay between laser and X-ray pulse is controlled electronically rangingfrom ps to seconds in time scale.

Sample delivery and triggering. An aspect to consider in a apump-probe experiment is that of delivering the sample to X-ray beampath, where also the sample is pumped by a laser of a given wavelengthperpendicular to the primary beam. The sample is pumped througha quartz capillary with inner diameter of 0.5 mm or 1 mm, (HamptonResearch), with a wall thickness of 10 µm. The thin walls are cru-cial for keeping the background scattering caused by the capillary toa minimum. The quarts capillary is flanked by heat shrink tubing toensure a good seal, and those tubings are connected to non-gas perme-able tubing. The samples are driven using a motorized syringe pump(neMESYS) which is electronically controlled to set a given flow-rateand to choose amount of volume that should be pumped in the for-ward and backward direction. It is crucial to continuously replace thesample volume between each pump-probe cycle, as the X-ray and laserenergy damages the protein samples, and thus the cycling of the sam-ple volume back and forth over a given distance allows for dilution ofsuch damage. Syringe pumps can cause anomalous scattering due toair bubbles as an an artifact of pumping the sample back and forth,partly due to pressure changes or bubbles of air passing through thecapillary, but is also a particular problem with detergents used formembrane protein samples. A peristaltic pump is less prone to givepump related artifacts in the scattering, as it’s always filled with liq-uid [211, p.19-20]. However, it seems that a larger quantity of sampleis required for such as system in order to pump through sample ina unidirectional fashion, which is difficult for membrane proteins, asthe expression may not yield such large volumes.

Rhodopsins are photoreceptor proteins with chromophores thatabsorb light at specific wavelengths, and thus the resting state is per-turbed by a laser pulse at a given wavelength to trigger the reaction.

5.3. Data processing

In regards to laser pulse energy used to excite the protein, sufficientamount of energy must be deposited in order to excite proteins whichare in the X-ray/laser intersect across the capillaries width. This isbecause the proteins in the sample should undergo structural changessimultaneously. If the power is too high, the solvent thermal responsewill be very large. A power titration can be performed to investigatethe signal strength as a function of laser power, until a point when itplateaus [211, p.17]. However, if an experiment involves the collectionof data for several proteins, this process can be to costly in terms oftime and sample. Also, the pulse energy also depends on the concen-tration of the sample, which can vary from different batches of thesame protein or between different proteins. Data from previous ex-periments, could provide a starting point for the laser pulse energy, inthe initial test runs, beginning from lower energy, and incrementallyincreased until a slight thermal response is visible from the solvent.

5.3 Data processingRadial integration and correction of detector images. Scat-tering data is collected on 2D integrating CCD detector, but thentransformed to a 1D curve via radial integration, or azimuthal aver-aging. As mentioned previously, the sample contains randomly ori-ented proteins and thus the scattering pattern should be isotropic,and scattering, uniform at a defined radius.

Due to the nature of synchrotron radiation being polarized, thescattering patttern will not be fully isotropic. Furthermore, certainpixels on the detector may have varying responsiveness compared tomost other pixels. Those pixels along with pixels close to the vicinityof the beamstop are usually masked. A CCD detector does not detectthe scattered X-ray photon directly, and uses instead a phosphorescentscreen as an intermediate between the X-ray photon and the detector[212]. In conjunction with radial integration several correction suchas polarized radiation, mask, detector distance, phosphorescence aretaken into account. These correction are usually done with the helpof the beamline staff, since they have a vast knowledge of the X-ray

once it reaches the limit of it’s dynamic range. The time-resolutionhere is defined by the length of a single X-ray pulse, which is 100 ps,and longer time-points can be achieved by varying the opening timesof the chopper, so it can transmit more than one X-ray pulse. The de-lay between laser and X-ray pulse is controlled electronically rangingfrom ps to seconds in time scale.

Sample delivery and triggering. An aspect to consider in a apump-probe experiment is that of delivering the sample to X-ray beampath, where also the sample is pumped by a laser of a given wavelengthperpendicular to the primary beam. The sample is pumped througha quartz capillary with inner diameter of 0.5 mm or 1 mm, (HamptonResearch), with a wall thickness of 10 µm. The thin walls are cru-cial for keeping the background scattering caused by the capillary toa minimum. The quarts capillary is flanked by heat shrink tubing toensure a good seal, and those tubings are connected to non-gas perme-able tubing. The samples are driven using a motorized syringe pump(neMESYS) which is electronically controlled to set a given flow-rateand to choose amount of volume that should be pumped in the for-ward and backward direction. It is crucial to continuously replace thesample volume between each pump-probe cycle, as the X-ray and laserenergy damages the protein samples, and thus the cycling of the sam-ple volume back and forth over a given distance allows for dilution ofsuch damage. Syringe pumps can cause anomalous scattering due toair bubbles as an an artifact of pumping the sample back and forth,partly due to pressure changes or bubbles of air passing through thecapillary, but is also a particular problem with detergents used formembrane protein samples. A peristaltic pump is less prone to givepump related artifacts in the scattering, as it’s always filled with liq-uid [211, p.19-20]. However, it seems that a larger quantity of sampleis required for such as system in order to pump through sample ina unidirectional fashion, which is difficult for membrane proteins, asthe expression may not yield such large volumes.

Rhodopsins are photoreceptor proteins with chromophores thatabsorb light at specific wavelengths, and thus the resting state is per-turbed by a laser pulse at a given wavelength to trigger the reaction.

In regards to laser pulse energy used to excite the protein, sufficientamount of energy must be deposited in order to excite proteins whichare in the X-ray/laser intersect across the capillaries width. This isbecause the proteins in the sample should undergo structural changessimultaneously. If the power is too high, the solvent thermal responsewill be very large. A power titration can be performed to investigatethe signal strength as a function of laser power, until a point when itplateaus [211, p.17]. However, if an experiment involves the collectionof data for several proteins, this process can be to costly in terms oftime and sample. Also, the pulse energy also depends on the concen-tration of the sample, which can vary from different batches of thesame protein or between different proteins. Data from previous ex-periments, could provide a starting point for the laser pulse energy, inthe initial test runs, beginning from lower energy, and incrementallyincreased until a slight thermal response is visible from the solvent.

5.3 Data processingRadial integration and correction of detector images. Scat-tering data is collected on 2D integrating CCD detector, but thentransformed to a 1D curve via radial integration, or azimuthal aver-aging. As mentioned previously, the sample contains randomly ori-ented proteins and thus the scattering pattern should be isotropic,and scattering, uniform at a defined radius.

Due to the nature of synchrotron radiation being polarized, thescattering patttern will not be fully isotropic. Furthermore, certainpixels on the detector may have varying responsiveness compared tomost other pixels. Those pixels along with pixels close to the vicinityof the beamstop are usually masked. A CCD detector does not detectthe scattered X-ray photon directly, and uses instead a phosphorescentscreen as an intermediate between the X-ray photon and the detector[212]. In conjunction with radial integration several correction suchas polarized radiation, mask, detector distance, phosphorescence aretaken into account. These correction are usually done with the helpof the beamline staff, since they have a vast knowledge of the X-ray

Figure 5.2: Radial integration

source and detector, and have often developed computational toolsfor these specific purposes.

Difference scattering curves. In the TRXSS experiment, scat-tering of resting and illuminated proteins at various time delays arerecorded, and the curved are subtracted for each time-point againstthe dark state to obtain difference curves. Since all atoms scatterfrom the solution sample, the scattering intensity is contributed bynot only the protein, but also the solvent and protein-solvent cross-terms. Difference scattering curves allow for contrast, and increasesthe structural sensitivity for the protein structural changes. As canbe seen in figure 5.3, the illuminated and dark state have very similarin terms of absolute scattering, and it is when we subtract the inten-sity of a given time-point with the dark that we obtain a structuralfingerprint of that given time-point.

The isosbestic point of water scattering with respect to heatingshows at q ∼ 1.5Å−1, and the scattering curves are normalised in thescattering range 1.4 < q < 1.6 Å−1 (q0) [76,177,186,213]. The absolutescattering normalisation in reference to the total scattering intensityrecorded at the angular region q0 can be described as,

Inormalised(q, ∆t) = I(q, ∆t)Σq0I(q, ∆t) (5.1)

Figure 5.3: Absolute scattering and difference X-ray scatteringLeft: Absolute scattering, of ground state superimposed with protein-micellecomplex being probed at 13.8 µs after illumination. Right: The subtractedlight-activated state from the ground state, showing the difference scatteringsignal. It can be seen that the difference scattering signal is much smallerthan that of the absolute scattering signal.

The difference calculation with regards to normalisation is de-scribed by equation 5.2,

∆I(q, ∆t) = Inormalised(q, ∆t) − Inormalised(q, ∆t ≤ 0) (5.2)

Errors associated with measurements are higher in difference curvesthan that of the absolute scattering, which affect the signal-to-noise[214]. A method to circumvent this and other background error suchas radiation damage and experimental drifts [211, p.22], is to inter-leave the laser off images with the laser on images when performingan experiment [215]. Other benefits of difference scattering curves in-clude (i) unchanged atomic scattering distribution which dominatesthe absolute scattering intensity, such as the buffer and capillary is re-moved in the difference calculation. (ii) systematic errors, inherent tothe experimental set-up or hardware are greatly reduced or completelyeliminated [214], and (iii), the contrast of a structural intermediate ata given time-point, is greatly enhanced in the difference scattering as

Figure 5.2: Radial integration

source and detector, and have often developed computational toolsfor these specific purposes.

Difference scattering curves. In the TRXSS experiment, scat-tering of resting and illuminated proteins at various time delays arerecorded, and the curved are subtracted for each time-point againstthe dark state to obtain difference curves. Since all atoms scatterfrom the solution sample, the scattering intensity is contributed bynot only the protein, but also the solvent and protein-solvent cross-terms. Difference scattering curves allow for contrast, and increasesthe structural sensitivity for the protein structural changes. As canbe seen in figure 5.3, the illuminated and dark state have very similarin terms of absolute scattering, and it is when we subtract the inten-sity of a given time-point with the dark that we obtain a structuralfingerprint of that given time-point.

The isosbestic point of water scattering with respect to heatingshows at q ∼ 1.5Å−1, and the scattering curves are normalised in thescattering range 1.4 < q < 1.6 Å−1 (q0) [76,177,186,213]. The absolutescattering normalisation in reference to the total scattering intensityrecorded at the angular region q0 can be described as,

Inormalised(q, ∆t) = I(q, ∆t)Σq0I(q, ∆t) (5.1)

Figure 5.3: Absolute scattering and difference X-ray scatteringLeft: Absolute scattering, of ground state superimposed with protein-micellecomplex being probed at 13.8 µs after illumination. Right: The subtractedlight-activated state from the ground state, showing the difference scatteringsignal. It can be seen that the difference scattering signal is much smallerthan that of the absolute scattering signal.

The difference calculation with regards to normalisation is de-scribed by equation 5.2,

∆I(q, ∆t) = Inormalised(q, ∆t) − Inormalised(q, ∆t ≤ 0) (5.2)

Errors associated with measurements are higher in difference curvesthan that of the absolute scattering, which affect the signal-to-noise[214]. A method to circumvent this and other background error suchas radiation damage and experimental drifts [211, p.22], is to inter-leave the laser off images with the laser on images when performingan experiment [215]. Other benefits of difference scattering curves in-clude (i) unchanged atomic scattering distribution which dominatesthe absolute scattering intensity, such as the buffer and capillary is re-moved in the difference calculation. (ii) systematic errors, inherent tothe experimental set-up or hardware are greatly reduced or completelyeliminated [214], and (iii), the contrast of a structural intermediate ata given time-point, is greatly enhanced in the difference scattering as

compared to the absolute scattering.Scattering cross-terms, from protein-solvent, and between protein-

micelle and micelle-solvent in solubilised membrane proteins are notremoved in the difference calculation, and must be accounted for instructural interpretation and modeling [216, p.20-21].

Thermal response of the solvent. When photoactivating pro-teins in solution using a laser, part of the energy will be absorbed fromthe photon, triggering the structural transitions, and some of the en-ergy will dissipate into the surrounding solution. Thermal expansionof the laser excited volume, alters the average distances between wa-ter molecules, which then affects the scattered signal. The absolutescattering signal has a positive peak at q ∼ 2 Å−1, and in the cal-culated difference curves, the q-range where the heat is scaled to thedata is defined as q = 1.5 − 2.1 Å−1. The regions that undergo lit-tle or no change in the difference scattering curve as a result of thesolvent thermal expansion can be used as normalisation regions (q0).The scattering of the protein structural changes is partly affected bythe heated solvent, as subtle changes may influence the protein signalin the difference curves. The scattering at each given time-delay, is acombination of a light-induced structural signal and a thermal signal.These two contributions need to be separated, and usually achievedby recording "pure heat" signal which we can use to subtract from themixture, as following,

∆Ilight(q, ∆t) = ∆Ilight+heat(q, ∆t) − k(∆t)∆Iheat(q) (5.3)

Where k(∆t) is the time-delay dependant scaling-factor, scaledto the regular experiment difference signal for each time-point, in aregion where water scattering is dominating (q > 1.5 Å−1) before it’ssubtracted (see figure 5.4).

There are different ways to obtain the pure heating signal (∆Iheat),which includes, (i) displacing the laser and X-ray beam such that thesnapshot of sample is obtained at another position across the lengthof the capillary, further away from the laser excited volume in the

Figure 5.4: Solvent heating The laser pump photon is absorbed by thechromophore, and the excess energy dissipates into the solvent as heat. Thethermal expansion of water causes a pronounced negative feature in thedifference scattering curve around 1.5 < q < 2.1 Å−1. Using an IR laser(red line), to heat up the solvent without triggering structural activationof the protein, so that the isolated thermal response can be obtained andsubtracted from the data (black line).

direction of the flow. (ii) In cases where the protein structural signaldecays at a much faster rate then the heating signal, longer time-points containing only the heat signal can be captured. (iii) Changingthe laser wavelength to the infrared (IR) region, allows us to heat thesolvent without triggering structural activation of the protein.

Removing Outliers. Some of the data collected at an experimentwill be corrupted. To reject such outliers we looked at the region 2 <q < 2.5 Å−1 as our range, where the protein signal is minuscule andwater scattering signal is dominant. The absolute scattering curvesthat were differing a few percent from the median scattering in thisregion is rejected, and the difference scattering images with a couple ofstandard deviations from the median scattering is considered outliersand rejected.

compared to the absolute scattering.Scattering cross-terms, from protein-solvent, and between protein-

micelle and micelle-solvent in solubilised membrane proteins are notremoved in the difference calculation, and must be accounted for instructural interpretation and modeling [216, p.20-21].

Thermal response of the solvent. When photoactivating pro-teins in solution using a laser, part of the energy will be absorbed fromthe photon, triggering the structural transitions, and some of the en-ergy will dissipate into the surrounding solution. Thermal expansionof the laser excited volume, alters the average distances between wa-ter molecules, which then affects the scattered signal. The absolutescattering signal has a positive peak at q ∼ 2 Å−1, and in the cal-culated difference curves, the q-range where the heat is scaled to thedata is defined as q = 1.5 − 2.1 Å−1. The regions that undergo lit-tle or no change in the difference scattering curve as a result of thesolvent thermal expansion can be used as normalisation regions (q0).The scattering of the protein structural changes is partly affected bythe heated solvent, as subtle changes may influence the protein signalin the difference curves. The scattering at each given time-delay, is acombination of a light-induced structural signal and a thermal signal.These two contributions need to be separated, and usually achievedby recording "pure heat" signal which we can use to subtract from themixture, as following,

∆Ilight(q, ∆t) = ∆Ilight+heat(q, ∆t) − k(∆t)∆Iheat(q) (5.3)

Where k(∆t) is the time-delay dependant scaling-factor, scaledto the regular experiment difference signal for each time-point, in aregion where water scattering is dominating (q > 1.5 Å−1) before it’ssubtracted (see figure 5.4).

There are different ways to obtain the pure heating signal (∆Iheat),which includes, (i) displacing the laser and X-ray beam such that thesnapshot of sample is obtained at another position across the lengthof the capillary, further away from the laser excited volume in the

Figure 5.4: Solvent heating The laser pump photon is absorbed by thechromophore, and the excess energy dissipates into the solvent as heat. Thethermal expansion of water causes a pronounced negative feature in thedifference scattering curve around 1.5 < q < 2.1 Å−1. Using an IR laser(red line), to heat up the solvent without triggering structural activationof the protein, so that the isolated thermal response can be obtained andsubtracted from the data (black line).

direction of the flow. (ii) In cases where the protein structural signaldecays at a much faster rate then the heating signal, longer time-points containing only the heat signal can be captured. (iii) Changingthe laser wavelength to the infrared (IR) region, allows us to heat thesolvent without triggering structural activation of the protein.

Removing Outliers. Some of the data collected at an experimentwill be corrupted. To reject such outliers we looked at the region 2 <q < 2.5 Å−1 as our range, where the protein signal is minuscule andwater scattering signal is dominant. The absolute scattering curvesthat were differing a few percent from the median scattering in thisregion is rejected, and the difference scattering images with a couple ofstandard deviations from the median scattering is considered outliersand rejected.

Monochromatic and polychromatic X-ray radiation Beam-lines utilize different instrumentation to use the synchrotron radia-tion for different tasks, depending on it’s specialized area of study.Beamlines such as ID09 at ESRF which focus on recording data withhigh temporal resolution will usually not use a monochromator, sincethe insertion reduces the number of photons by almost a factor of10. Therefore, the radiation at such a beamline will be polychro-matic, meaning that the radiation contains a number of different wave-lengths. This should be taken into consideration when comparing theexperimental data to predicted scattering [177]. Luckily, a monochro-matic scattering curve can be transformed to a polychromatic scat-tering curve when the polychromatic X-ray wavelength distribution isknown [217].

5.4 Structural analysis and modelingLinear decomposition and basis spectra. A TRXSS experimentprobes the protein over a time range, and the resulting difference curvewill contain multiple time points. It follows that we want to reducethe complexity of this data, to better understand how many uniquepopulations the data contains, which it can be reduced to. The TRXSSdifference scattering curves can be seen as a linear combination of a setof basis spectra which represents the difference scattering of a givenintermediate. The structural signal in the difference scattering over alltime-delay represents a mixture of protein conformation fingerprints,each forming and decaying at different rates. Linear decompositionallows us to identify the number of independent structural componentsnecessary to reconstruct the data. The basis spectra can be obtainedby different methods such as singular value decomposition (SVD), andkinetic analysis, where a kinetic model is imposed on the differencescattering data, to provide information about the rates at which astructural component forms and decays over time. These methods arefrequently used in combination for the decomposition of TRXSS data.

5.4. Structural analysis and modeling

Predicting the X-ray scattering from atomic coordinates.The principal ambition of TRXSS data analysis is the extraction ofkinetic and structural information from the experimentally measureddifference intensities (∆I(q, t)exp), by reproducing the experimentaldata in terms of theoretical data (∆I(q, t)theory). In order to extractstructural information from the decomposed time-independent basisspectra, we need to predict the X-ray solution scattering from atomiccoordinates. Several software suits exist to predict scattering such as(CRYSOL, SASTBX, WAXSiS [172, 218, 219]). CRYSOL [172] wasused in Paper I-III to calculate the scattering from models generatedwith MD simulations.

CRYSOL was the first major software-suit to be released for pre-diction of small-angle X-ray scattering (SAXS) back in 1995, and ispossibly to this day still one of the most used programs for X-rayscattering prediction from proteins in solution. CRYSOL evaluatesthe scattering intensity directly from atomic coordinates, by calculat-ing the spherically averaged scattering pattern,

I(q) = 〈|Fa(q) − ρ0Fe(q) + δρFb(q)|2〉 (5.4)

Where Fa(q) is the scattering amplitude of the protein in vacuo,equivalent to equation 4.8. The density of the solvent is representedby ρ0 which for water is 0.334 e/Å3, Fe(q) and Fb(q) are the scatter-ing amplitudes from the excluded solvent and hydration layer, eachwith a specific density δρ = ρb − ρ0. For wide angle scattering, thecontribution of the hydration layer is minimal, as it mainly affects thescattering curve at q < 0.1 Å−1 (see figure 5.5).

Micelle forming detergents are routinely used to mimic the cellmembrane, where the hydrophobic regions of the protein region forma tight complex with the detergent micelle to make a protein-detergentcomplex (PDC) [220]. The experimental scattering intensity from theparticle therefore contains the contribution from both protein and mi-celle. The X-ray scattering correlations between the membrane proteinand membrane micelle is typically observed as a strong peak in the

Monochromatic and polychromatic X-ray radiation Beam-lines utilize different instrumentation to use the synchrotron radia-tion for different tasks, depending on it’s specialized area of study.Beamlines such as ID09 at ESRF which focus on recording data withhigh temporal resolution will usually not use a monochromator, sincethe insertion reduces the number of photons by almost a factor of10. Therefore, the radiation at such a beamline will be polychro-matic, meaning that the radiation contains a number of different wave-lengths. This should be taken into consideration when comparing theexperimental data to predicted scattering [177]. Luckily, a monochro-matic scattering curve can be transformed to a polychromatic scat-tering curve when the polychromatic X-ray wavelength distribution isknown [217].

5.4 Structural analysis and modelingLinear decomposition and basis spectra. A TRXSS experimentprobes the protein over a time range, and the resulting difference curvewill contain multiple time points. It follows that we want to reducethe complexity of this data, to better understand how many uniquepopulations the data contains, which it can be reduced to. The TRXSSdifference scattering curves can be seen as a linear combination of a setof basis spectra which represents the difference scattering of a givenintermediate. The structural signal in the difference scattering over alltime-delay represents a mixture of protein conformation fingerprints,each forming and decaying at different rates. Linear decompositionallows us to identify the number of independent structural componentsnecessary to reconstruct the data. The basis spectra can be obtainedby different methods such as singular value decomposition (SVD), andkinetic analysis, where a kinetic model is imposed on the differencescattering data, to provide information about the rates at which astructural component forms and decays over time. These methods arefrequently used in combination for the decomposition of TRXSS data.

Predicting the X-ray scattering from atomic coordinates.The principal ambition of TRXSS data analysis is the extraction ofkinetic and structural information from the experimentally measureddifference intensities (∆I(q, t)exp), by reproducing the experimentaldata in terms of theoretical data (∆I(q, t)theory). In order to extractstructural information from the decomposed time-independent basisspectra, we need to predict the X-ray solution scattering from atomiccoordinates. Several software suits exist to predict scattering such as(CRYSOL, SASTBX, WAXSiS [172, 218, 219]). CRYSOL [172] wasused in Paper I-III to calculate the scattering from models generatedwith MD simulations.

CRYSOL was the first major software-suit to be released for pre-diction of small-angle X-ray scattering (SAXS) back in 1995, and ispossibly to this day still one of the most used programs for X-rayscattering prediction from proteins in solution. CRYSOL evaluatesthe scattering intensity directly from atomic coordinates, by calculat-ing the spherically averaged scattering pattern,

I(q) = 〈|Fa(q) − ρ0Fe(q) + δρFb(q)|2〉 (5.4)

Where Fa(q) is the scattering amplitude of the protein in vacuo,equivalent to equation 4.8. The density of the solvent is representedby ρ0 which for water is 0.334 e/Å3, Fe(q) and Fb(q) are the scatter-ing amplitudes from the excluded solvent and hydration layer, eachwith a specific density δρ = ρb − ρ0. For wide angle scattering, thecontribution of the hydration layer is minimal, as it mainly affects thescattering curve at q < 0.1 Å−1 (see figure 5.5).

Micelle forming detergents are routinely used to mimic the cellmembrane, where the hydrophobic regions of the protein region forma tight complex with the detergent micelle to make a protein-detergentcomplex (PDC) [220]. The experimental scattering intensity from theparticle therefore contains the contribution from both protein and mi-celle. The X-ray scattering correlations between the membrane proteinand membrane micelle is typically observed as a strong peak in the

Figure 5.5: Hydration layer. (A) Sverguns original schematic illustra-tion showing the protein and hydration layer as modeled by CRYSOL,where ρ0 is the bulk solvent, ρb is the uniform density of the border layer(hydration layer), it’s thicknesss is ∆, and the angular function which de-scribes the envelope is F (ω). The image was directly copied from Svergun etal [172] with permission from the International Union of Crystallography.(B) Berntsson,O illustrated the effects of altering the density contrast of thehydration layer around Drosophila melanogaster chryptochrome while keep-ing the structure constant, and adding or removing a few water molecules,drastically alters the scattering at low angles, but mainly at q < 0.1 Å−1.Picture directly copied from Berntsson,O [211, p.13,fig 2.4B]

q-range ∼ 0.17 < q ≤ 0.22 Å−1. Furthermore, as the protein is acti-vated and proceeds through it’s transient intermediates, the "inner"part of the micelle closest to the protein undergoes systematic changeas a function of protein structural changes. This cross-term was pre-viously omitted completely from the analysis, with the assumptionthat the changes in micelle structure upon activation are negligible ascompared to the protein structural changes [216, p.24], but for whichwe try to account for in this thesis. Furthermore, previous work byMalmerberg, assumed the change in excluded volume for the restingand excited state are small [216, p.24]. CRYSOL was designed pri-marily for soluble proteins, and not proteins solubilised in detergent.The term ρ0Fe(q) from equation 5.4 is the solvent density (ρ0) andscattering amplitude for the excluded solvent (Fe(q)), which accounts

for the solvent density of water but not the micelle. Because the mi-celle has a different density than that of water, the density contrastfor the protein-micelle solvent excluded volume was a factor that wastaken into account for the analysis.

Information content. As mentioned in chapter ??, different re-gions of q space correlate to approximate distances in real space, asshown in figure 5.6. At small angles, corresponding to region I, elec-trons scatter in phase, giving rise to a large scattering intensity in thisregion. In equation 4.7, q < 2π/r, we could see that smaller distancescontributes to scattering at higher angles whereas longer distancescontributes to smaller angles. The structural information content inthis region correlates to the overall shape and molecular envelope ofa macromolecule such as the radius of gyration (Rg) [168], and alsomolecular information such as rmax, which is the largest internal dis-tance in the particle.

Figure 5.6: Structural information from TR-XSS A) bR resting stateconformation (green), rigid body intermediate (cyan). B) Relationship be-tween reciprocal space and real space. Each region in q space is correlated torelative distances in real space as indicated by the roman numerals betweenpanels A and B. Directly copied from Andersson et al. with permissionlicence from Elsevier publishers.

Figure 5.5: Hydration layer. (A) Sverguns original schematic illustra-tion showing the protein and hydration layer as modeled by CRYSOL,where ρ0 is the bulk solvent, ρb is the uniform density of the border layer(hydration layer), it’s thicknesss is ∆, and the angular function which de-scribes the envelope is F (ω). The image was directly copied from Svergun etal [172] with permission from the International Union of Crystallography.(B) Berntsson,O illustrated the effects of altering the density contrast of thehydration layer around Drosophila melanogaster chryptochrome while keep-ing the structure constant, and adding or removing a few water molecules,drastically alters the scattering at low angles, but mainly at q < 0.1 Å−1.Picture directly copied from Berntsson,O [211, p.13,fig 2.4B]

q-range ∼ 0.17 < q ≤ 0.22 Å−1. Furthermore, as the protein is acti-vated and proceeds through it’s transient intermediates, the "inner"part of the micelle closest to the protein undergoes systematic changeas a function of protein structural changes. This cross-term was pre-viously omitted completely from the analysis, with the assumptionthat the changes in micelle structure upon activation are negligible ascompared to the protein structural changes [216, p.24], but for whichwe try to account for in this thesis. Furthermore, previous work byMalmerberg, assumed the change in excluded volume for the restingand excited state are small [216, p.24]. CRYSOL was designed pri-marily for soluble proteins, and not proteins solubilised in detergent.The term ρ0Fe(q) from equation 5.4 is the solvent density (ρ0) andscattering amplitude for the excluded solvent (Fe(q)), which accounts

for the solvent density of water but not the micelle. Because the mi-celle has a different density than that of water, the density contrastfor the protein-micelle solvent excluded volume was a factor that wastaken into account for the analysis.

Information content. As mentioned in chapter ??, different re-gions of q space correlate to approximate distances in real space, asshown in figure 5.6. At small angles, corresponding to region I, elec-trons scatter in phase, giving rise to a large scattering intensity in thisregion. In equation 4.7, q < 2π/r, we could see that smaller distancescontributes to scattering at higher angles whereas longer distancescontributes to smaller angles. The structural information content inthis region correlates to the overall shape and molecular envelope ofa macromolecule such as the radius of gyration (Rg) [168], and alsomolecular information such as rmax, which is the largest internal dis-tance in the particle.

Figure 5.6: Structural information from TR-XSS A) bR resting stateconformation (green), rigid body intermediate (cyan). B) Relationship be-tween reciprocal space and real space. Each region in q space is correlated torelative distances in real space as indicated by the roman numerals betweenpanels A and B. Directly copied from Andersson et al. with permissionlicence from Elsevier publishers.

Regions II and III, that are intermediate to wide-angles, resolveintermolecular distances between secondary structure, between do-mains and individual α-helices and β-sheets within the protein. Inregion IV, local distances between individual side chains contributeto the scattering intensity, and it reaches the limits of resolution typi-cally for macromolecules such as proteins, as q > 1.2 Å−1 is dominatedby solvent scattering and limited scattering intensity from the proteindue to deconstructive interference. The experimental difference curvesshow an oscillating pattern, altering in intensity across the q-region,corresponding to the formation and depletion of given distances inthe protein. As discussed earlier, the goal of structural analysis, is thereconstruction of the experimental data in terms of theoretical data.This allows us to pin-point the coordinates of three-dimensional struc-tural changes related to experimental, one-dimensional basis spectra,indicating the conformational fingerprint of a given intermediate.

Rigid body modeling One method of obtaining detailed struc-tural information, is the use of high resolution structures of resting,intermediate or active states of a protein, afforded from either con-ventional or serial crystallography. Rigid body modeling was adoptedand used in the earlier strategies for modeling, and Andersson et al.were the first to apply TRXSS for the study of conformational changesin membrane proteins, and recorded time-resolved scattering data onbacteriorhodopsin and proteorhodopsin [178]. Using available crystalstructures, they created rigid body intermediates by calculating a dif-ference vector between ground and activated protein, which is thenmultiplied by an integer to create a new intermediate. These inter-mediates are then refined in orientation and position iteratively, bychanging the extent of structural movements of given residues on agrid. Theoretical X-ray scattering prediction software like CRYSOLwas then used on the rigid bodies and difference scattering calculatedfor fitting against experimental basis spectra. Similar approaches tothis have been used multiple times since [185, 221–223]. X-ray scat-tering prediction from rigid body intermediates alone, have severaldrawbacks. The first one is the restricted conformational samplingspace. Also, the models themselves can be difficult to judge whether

or not they are physically reasonable, especially if no other measuresare taken to ensure this. Malmerberg et al. [224] added a regularisa-tion step to the refinement algorithm to alleviate this problem, whichadjusted the bond lengths, angles, dihedral angles to reasonable val-ues.

The other major drawback of X-ray prediction from atom coordi-nates of single rigid body intermediates, is that the difference calcula-tion curve does not capture the equilibrium fluctuation of that givenintermediate, as seen in the experimental data.

Molecular Dynamics. The implementation of molecular dynam-ics (MD) in analysis of TRXSS data is useful when investigating dy-namics associated with protein conformational changes. Initially whenMD was developed in the 1970’s, the typical trajectory was a few pi-coseconds [125]. The growth in computational power and high perfor-mance computing has extended the simulation time from picosecondsto hundreds of nanoseconds, sampling molecular motions on severalmicrosecond time scale, and ensemble techniques that can study pro-tein folding in the several millisecond time scale [225]. For a moleculardynamics simulation, an in silico system is constructed, which shouldresemble the conditions of the experimental system as accurately aspossible. In this thesis, an atomic model of a given protein moleculewas constructed in complex with it’s respective detergent, used inthe experiment, along with solvent molecules and ions. From atomicpositions, mass and knowledge of the forces governing their interac-tions, Newtons equations of motion can be solved, numerically andstepwise [226]. The time-steps are in femtoseconds (10−15) and thetrajectory of the system continues typically for several nanosecondsor longer. These simulations are classical, meaning that they are notquantum mechanical calculations. The forces are calculated from thepotential energy, governed by the atomic coordinates and the chosenforce field. The force field defines the restraints in terms of bondedand non-bonded interactions, such as bond angles, bond lengths, di-hedral angles, bond stretching etc. ensuring that the protein is phys-ically sensible. In this thesis, the CHARMM36 force field was usedto construct the protein-micelle complex via the CHARMM-GUI web

Regions II and III, that are intermediate to wide-angles, resolveintermolecular distances between secondary structure, between do-mains and individual α-helices and β-sheets within the protein. Inregion IV, local distances between individual side chains contributeto the scattering intensity, and it reaches the limits of resolution typi-cally for macromolecules such as proteins, as q > 1.2 Å−1 is dominatedby solvent scattering and limited scattering intensity from the proteindue to deconstructive interference. The experimental difference curvesshow an oscillating pattern, altering in intensity across the q-region,corresponding to the formation and depletion of given distances inthe protein. As discussed earlier, the goal of structural analysis, is thereconstruction of the experimental data in terms of theoretical data.This allows us to pin-point the coordinates of three-dimensional struc-tural changes related to experimental, one-dimensional basis spectra,indicating the conformational fingerprint of a given intermediate.

Rigid body modeling One method of obtaining detailed struc-tural information, is the use of high resolution structures of resting,intermediate or active states of a protein, afforded from either con-ventional or serial crystallography. Rigid body modeling was adoptedand used in the earlier strategies for modeling, and Andersson et al.were the first to apply TRXSS for the study of conformational changesin membrane proteins, and recorded time-resolved scattering data onbacteriorhodopsin and proteorhodopsin [178]. Using available crystalstructures, they created rigid body intermediates by calculating a dif-ference vector between ground and activated protein, which is thenmultiplied by an integer to create a new intermediate. These inter-mediates are then refined in orientation and position iteratively, bychanging the extent of structural movements of given residues on agrid. Theoretical X-ray scattering prediction software like CRYSOLwas then used on the rigid bodies and difference scattering calculatedfor fitting against experimental basis spectra. Similar approaches tothis have been used multiple times since [185, 221–223]. X-ray scat-tering prediction from rigid body intermediates alone, have severaldrawbacks. The first one is the restricted conformational samplingspace. Also, the models themselves can be difficult to judge whether

or not they are physically reasonable, especially if no other measuresare taken to ensure this. Malmerberg et al. [224] added a regularisa-tion step to the refinement algorithm to alleviate this problem, whichadjusted the bond lengths, angles, dihedral angles to reasonable val-ues.

The other major drawback of X-ray prediction from atom coordi-nates of single rigid body intermediates, is that the difference calcula-tion curve does not capture the equilibrium fluctuation of that givenintermediate, as seen in the experimental data.

Molecular Dynamics. The implementation of molecular dynam-ics (MD) in analysis of TRXSS data is useful when investigating dy-namics associated with protein conformational changes. Initially whenMD was developed in the 1970’s, the typical trajectory was a few pi-coseconds [125]. The growth in computational power and high perfor-mance computing has extended the simulation time from picosecondsto hundreds of nanoseconds, sampling molecular motions on severalmicrosecond time scale, and ensemble techniques that can study pro-tein folding in the several millisecond time scale [225]. For a moleculardynamics simulation, an in silico system is constructed, which shouldresemble the conditions of the experimental system as accurately aspossible. In this thesis, an atomic model of a given protein moleculewas constructed in complex with it’s respective detergent, used inthe experiment, along with solvent molecules and ions. From atomicpositions, mass and knowledge of the forces governing their interac-tions, Newtons equations of motion can be solved, numerically andstepwise [226]. The time-steps are in femtoseconds (10−15) and thetrajectory of the system continues typically for several nanosecondsor longer. These simulations are classical, meaning that they are notquantum mechanical calculations. The forces are calculated from thepotential energy, governed by the atomic coordinates and the chosenforce field. The force field defines the restraints in terms of bondedand non-bonded interactions, such as bond angles, bond lengths, di-hedral angles, bond stretching etc. ensuring that the protein is phys-ically sensible. In this thesis, the CHARMM36 force field was usedto construct the protein-micelle complex via the CHARMM-GUI web

interface [227–229]. Averaging over an equilibrium MD trajectory, al-lows the extraction of conformational changes over time in a givenspace. This relates to the ergodic hypothesis, that the time average ofa system is equivalent to the ensemble average. Thus, MD simulationsin conjunction with TRXSS data can provide meaningful insights intolight induced protein structural changes and transient intermediates.

Molecular dynamics guided by experimental X-ray scat-tering data. In guided MD a subset of atoms in the simulation isguided towards a final ’target’ structure by means of altering the en-ergy landscape imposed by the used force field. After first aligningthe target structure to the current coordinates, the RMS distance be-tween the current coordinates and the target structure is calculated foreach time step. When done correctly, the simulation will bias towardsa structure which is in agreement with the scattering data withoutbreaking the physical restraints of the forcefield, and thus producinga physically sensible structure. In 2009, Ahn et al. [179] analysed a dif-ference scattering curve of myoglobin at 10 ns after carbon monoxidephotolysis, by using rigid-body guided MD simulations. An extensionof this method was used by Björling et al. [192], implementing a X-ray scattering guided MD tool for the software GROMACS. The im-plemenentation was unfortunately not carried through successive ver-sions of the MD software GROMACS. A drawback with the analysisscheme was its difficulty for parallelization, and choosing how the MDforcefield is weighted against the experimental scattering data. Fur-thermore, the previous analysis using X-ray scattering guided targetMD simulations, was performed on light activated protein that weresoluble, and thus did not account for proteins in complex with deter-gent micelles. Guiding MD similations have been proven to performwell when studying larger tertiary structural changes in the protein,even if those changes are very small [207]. The further developementof this method to encompass integral membrane proteins solubilizedin a detergent micelle, would allow it to become a good strategy foranalysing TRXSS data.

Chapter 6

Results and Discussion

6.1 Modelling difference X-ray scattering ob-servations from an integral membrane pro-tein within a detergent micelle (PAPERI)

Bacteriorhodopsin was the first discovered microbial rhodopsin, and isthe most well studied and characterized retinylidine protein. Found inthe purple membrane of Halobacterium salinarum, when illuminated,bR molecules harness the energy of light to translocate protons, cre-ating a electrochemical proton gradient across the membrane whichdrives the synthesis of ATP, the universal biological energy currency.

Time-resolved X-ray solution scattering (TR-XSS) is a branch ofstructural biology which aims to probe the conformational dynamicsof proteins as the evolve through their transient intermediates to ful-fill their function. The advent of serial crystallography revolutionisedserial time-resolved crystallography, and can capture structural detailof an evolving reaction from sub-picoseconds to minutes with high-resolution. However, samples must be crystallized, which is not al-ways possible, especially for intrinsically disordered proteins or dueto unknown crystallization conditions. The sample has to remain ina crystalline state along the progression of the reaction, which mightbe slowed down in the crystalline state. Lastly, the protein conforma-tional changes that are incompatible with the crystal lattice packing

interface [227–229]. Averaging over an equilibrium MD trajectory, al-lows the extraction of conformational changes over time in a givenspace. This relates to the ergodic hypothesis, that the time average ofa system is equivalent to the ensemble average. Thus, MD simulationsin conjunction with TRXSS data can provide meaningful insights intolight induced protein structural changes and transient intermediates.

Molecular dynamics guided by experimental X-ray scat-tering data. In guided MD a subset of atoms in the simulation isguided towards a final ’target’ structure by means of altering the en-ergy landscape imposed by the used force field. After first aligningthe target structure to the current coordinates, the RMS distance be-tween the current coordinates and the target structure is calculated foreach time step. When done correctly, the simulation will bias towardsa structure which is in agreement with the scattering data withoutbreaking the physical restraints of the forcefield, and thus producinga physically sensible structure. In 2009, Ahn et al. [179] analysed a dif-ference scattering curve of myoglobin at 10 ns after carbon monoxidephotolysis, by using rigid-body guided MD simulations. An extensionof this method was used by Björling et al. [192], implementing a X-ray scattering guided MD tool for the software GROMACS. The im-plemenentation was unfortunately not carried through successive ver-sions of the MD software GROMACS. A drawback with the analysisscheme was its difficulty for parallelization, and choosing how the MDforcefield is weighted against the experimental scattering data. Fur-thermore, the previous analysis using X-ray scattering guided targetMD simulations, was performed on light activated protein that weresoluble, and thus did not account for proteins in complex with deter-gent micelles. Guiding MD similations have been proven to performwell when studying larger tertiary structural changes in the protein,even if those changes are very small [207]. The further developementof this method to encompass integral membrane proteins solubilizedin a detergent micelle, would allow it to become a good strategy foranalysing TRXSS data.

Chapter 6

Results and Discussion

6.1 Modelling difference X-ray scattering ob-servations from an integral membrane pro-tein within a detergent micelle (PAPERI)

Bacteriorhodopsin was the first discovered microbial rhodopsin, and isthe most well studied and characterized retinylidine protein. Found inthe purple membrane of Halobacterium salinarum, when illuminated,bR molecules harness the energy of light to translocate protons, cre-ating a electrochemical proton gradient across the membrane whichdrives the synthesis of ATP, the universal biological energy currency.

Time-resolved X-ray solution scattering (TR-XSS) is a branch ofstructural biology which aims to probe the conformational dynamicsof proteins as the evolve through their transient intermediates to ful-fill their function. The advent of serial crystallography revolutionisedserial time-resolved crystallography, and can capture structural detailof an evolving reaction from sub-picoseconds to minutes with high-resolution. However, samples must be crystallized, which is not al-ways possible, especially for intrinsically disordered proteins or dueto unknown crystallization conditions. The sample has to remain ina crystalline state along the progression of the reaction, which mightbe slowed down in the crystalline state. Lastly, the protein conforma-tional changes that are incompatible with the crystal lattice packing

Chapter 6. Results and Discussion

are either restrained or destroy the crystal order and can thereforenot be observed. TR-XSS can principally avoid many of the limi-tations of crystal based approaches, and the method can measurestructural intermediates in real time and at room temperature. Incontrary, because the sample contains proteins randomly oriented insolution, the resolution is rather low and the measured information isone-dimensional. Due to lower information content, structural mod-elling relies on low-resolution modeling of the molecular envelope andshape construction or requires additional information from crystal-lographic structures in conjunction with molecular dynamics simu-lations. Furthermore, structural modelling and interpretation of in-tegral membrane proteins from TR-XSS recorded data, raises morechallenges, as a result of the detergent micelle which is in complexwith the protein, contains more atoms than the protein itself, andfluctuates randomly. This discrepancy between computational differ-ence scattering data and experimental TR-XSS scattering data is dueto averaging. The experimental TR-XSS data is reproducible becausethe unsystematic fluctuations of the detergent micelle, average outover the vast number of proteins being probed by the X-ray beam ata given experiment. A sample with a concentration of 0.5 mM containsabout 1013 protein molecules in a 35 nl sample volume. Attemptingto achieve the same averaging computationally by performing 1013

molecular dynamics (MD) simulations of protein-micelle systems, isnot feasible, and thus, approximations are necessary.

Even though most of the micelle fluctuations are mostly random,there is a cross-term between the protein and micelle, describing theinterference between the protein and the membrane. In our earliermodelling, it was assumed that this cross-term does not influencethe structural conclusions characterized from the difference scatter-ing data in the domain 0.2 Å−1 ≤ q ≤ 1.0 Å−1. In this paper, wecritically examine these assumptions by reanalyzing data from bacte-riorhodpsin and fitting that against structures obtained from molec-ular dynamics simulations of a protein in complex with a detergentmicelle, taking into consideration the change in the X-ray scatteringcross-term between the protein and detergent micelle as the protein

6.1. Modelling difference X-ray scattering observations from anintegral membrane protein within a detergent micelle (PAPER I)

changes structure. The analysis will shed light on the advantages anddrawbacks of our earlier approaches and yields a theoretical founda-tion for which to extend the structural analysis of integral membraneproteins using TR-XSS.

TR-XSS basis spectra. Linear decomposition of data from 2009after removal of heating, afforded two basis spectra (figure 6.3B) , withthe two populations intersecting at ∆t ≈ 35µs, (figure 6.3E whichgiven the limited time-sampling is close to the time-scale for the in-tersection of two populations as shown by time-resolved serial crystal-lography (TR-SFX) data of bacteriorhodpsin at ∆t ≈ 13µs [36], andcorresponds well with the L-M spectral transition in the photocycle(figure 6.3F. Laser-induced heating was removed using IR excitation,which in our previous work was subtracted using the ∆t = 100 mstime-delay. However, when compared to IR excitation shows quite alarge discrepancy for q = 0.3 Å−1 (figure 6.3A). The amplitude offthe XSS signal in terms of intensity of the 2017 data was only 40% ofthe amplitude shown in the 2009 data (figure 6.3C,D), and therefore,linear decomposition of the 2017 difference scattering data, failed toyield a conclusive difference signal for the first component. However,the second component extracted from 2017 TR-XSS difference datawas in very good agreement with that of the second component of2019 (97% Pearson correlation coefficient), see figure 6.3B.

Despite variations in signal-to-noise ratios, detergent concentra-tion, photo-excitation levels, and other factors associated with sam-ple preparation or experimental set-up, the measured difference curvesfrom the XSS data of light-sensitive integral membrane proteins arehighly reproducible. However, we still suggest that optimizing spa-tial overlap between laser focus and X-ray beam should be done inaddition to the optimisation of other parameters such as protein con-centration, thickness of capillary, and pump laser fluence, should bedone prior to collection of TR-XSS data.

are either restrained or destroy the crystal order and can thereforenot be observed. TR-XSS can principally avoid many of the limi-tations of crystal based approaches, and the method can measurestructural intermediates in real time and at room temperature. Incontrary, because the sample contains proteins randomly oriented insolution, the resolution is rather low and the measured information isone-dimensional. Due to lower information content, structural mod-elling relies on low-resolution modeling of the molecular envelope andshape construction or requires additional information from crystal-lographic structures in conjunction with molecular dynamics simu-lations. Furthermore, structural modelling and interpretation of in-tegral membrane proteins from TR-XSS recorded data, raises morechallenges, as a result of the detergent micelle which is in complexwith the protein, contains more atoms than the protein itself, andfluctuates randomly. This discrepancy between computational differ-ence scattering data and experimental TR-XSS scattering data is dueto averaging. The experimental TR-XSS data is reproducible becausethe unsystematic fluctuations of the detergent micelle, average outover the vast number of proteins being probed by the X-ray beam ata given experiment. A sample with a concentration of 0.5 mM containsabout 1013 protein molecules in a 35 nl sample volume. Attemptingto achieve the same averaging computationally by performing 1013

molecular dynamics (MD) simulations of protein-micelle systems, isnot feasible, and thus, approximations are necessary.

Even though most of the micelle fluctuations are mostly random,there is a cross-term between the protein and micelle, describing theinterference between the protein and the membrane. In our earliermodelling, it was assumed that this cross-term does not influencethe structural conclusions characterized from the difference scatter-ing data in the domain 0.2 Å−1 ≤ q ≤ 1.0 Å−1. In this paper, wecritically examine these assumptions by reanalyzing data from bacte-riorhodpsin and fitting that against structures obtained from molec-ular dynamics simulations of a protein in complex with a detergentmicelle, taking into consideration the change in the X-ray scatteringcross-term between the protein and detergent micelle as the protein

changes structure. The analysis will shed light on the advantages anddrawbacks of our earlier approaches and yields a theoretical founda-tion for which to extend the structural analysis of integral membraneproteins using TR-XSS.

TR-XSS basis spectra. Linear decomposition of data from 2009after removal of heating, afforded two basis spectra (figure 6.3B) , withthe two populations intersecting at ∆t ≈ 35µs, (figure 6.3E whichgiven the limited time-sampling is close to the time-scale for the in-tersection of two populations as shown by time-resolved serial crystal-lography (TR-SFX) data of bacteriorhodpsin at ∆t ≈ 13µs [36], andcorresponds well with the L-M spectral transition in the photocycle(figure 6.3F. Laser-induced heating was removed using IR excitation,which in our previous work was subtracted using the ∆t = 100 mstime-delay. However, when compared to IR excitation shows quite alarge discrepancy for q = 0.3 Å−1 (figure 6.3A). The amplitude offthe XSS signal in terms of intensity of the 2017 data was only 40% ofthe amplitude shown in the 2009 data (figure 6.3C,D), and therefore,linear decomposition of the 2017 difference scattering data, failed toyield a conclusive difference signal for the first component. However,the second component extracted from 2017 TR-XSS difference datawas in very good agreement with that of the second component of2019 (97% Pearson correlation coefficient), see figure 6.3B.

Despite variations in signal-to-noise ratios, detergent concentra-tion, photo-excitation levels, and other factors associated with sam-ple preparation or experimental set-up, the measured difference curvesfrom the XSS data of light-sensitive integral membrane proteins arehighly reproducible. However, we still suggest that optimizing spa-tial overlap between laser focus and X-ray beam should be done inaddition to the optimisation of other parameters such as protein con-centration, thickness of capillary, and pump laser fluence, should bedone prior to collection of TR-XSS data.

Figure 6.1: TR-XSS data recorded from sample containing bacte-riorhodpsin solubilized in detergent micelle A) Infrared light inducedchanges in difference X-ray scattering (red line) superimposed onto differ-ence XSS curve recorded for ∆t = 100 ms (black line and previously usedto indicate influence of heating [178]). B) Difference XSS basis spectra ex-tracted from TR-XSS data after removal of heat. Basis spectra for State 2(2009 data, black line) and 2017 (mustard line) in strong agreement. State1 basis spectra (blue line) extracted only from 2009 data after removal ofheat). C) TR-XSS data recorded in 2017 after removing the heat contri-bution. D) TR-XSS data from 2009 after removing the heat contribution.E) Time-scales for the growth and decay of State 1 (blue) and 2 (blackline) from 2009 data after removal heat contribution. F) Time-scales forgrowth and decay of distinct populations extracted from TR-SFX data,where three transient intermediates were obtained [36]. Time evolution ofspectral changes associated with growth and decay of the M-intermediateof bR photocycle is shown in mustard.

Figure 6.2: Bacteriorhodopsin intermediates. Movements of Cα-atoms of helix C from Pro77 to Pro91 extracted from pdb entries 5b6zminus 5b6v; and Cα-atom movements for helices E and F from Leu152to Pro186, extracted from pdb entries 6rph minus 6rpq. These movementsin different amplitudes and combinations were used for target moleculardynamics simulations using GROMACS [230].

Influence of the detergent micelle on difference X-rayscattering. In order to model difference X-ray scattering basis spec-tra from heat-corrected TR-XSS data 6.3C,D, we must be able topredict X-ray scattering changes from atomic coordinates for a setsecondary structural changes. The agreement between the theoreti-cal difference scattering prediction and the experimental differencespectra is optimized by sampling various amplitudes and combina-tion of the chosen motions. These candidate motions were simulatedwith a bacteriorhodopsin placed within a detergent micelle of β-octylglucoside, in order to investigate the scattering influences of proteinconformational changes on the micelle and vice-versa, but also thechanges in phase relationships between the X-ray scattering from thedetergent micelle and protein.

Since the protein X-ray scattering contribution from the , or in-tensity is proportional to |F (q)|2, the scattering contribution from the

Figure 6.1: TR-XSS data recorded from sample containing bacte-riorhodpsin solubilized in detergent micelle A) Infrared light inducedchanges in difference X-ray scattering (red line) superimposed onto differ-ence XSS curve recorded for ∆t = 100 ms (black line and previously usedto indicate influence of heating [178]). B) Difference XSS basis spectra ex-tracted from TR-XSS data after removal of heat. Basis spectra for State 2(2009 data, black line) and 2017 (mustard line) in strong agreement. State1 basis spectra (blue line) extracted only from 2009 data after removal ofheat). C) TR-XSS data recorded in 2017 after removing the heat contri-bution. D) TR-XSS data from 2009 after removing the heat contribution.E) Time-scales for the growth and decay of State 1 (blue) and 2 (blackline) from 2009 data after removal heat contribution. F) Time-scales forgrowth and decay of distinct populations extracted from TR-SFX data,where three transient intermediates were obtained [36]. Time evolution ofspectral changes associated with growth and decay of the M-intermediateof bR photocycle is shown in mustard.

Figure 6.2: Bacteriorhodopsin intermediates. Movements of Cα-atoms of helix C from Pro77 to Pro91 extracted from pdb entries 5b6zminus 5b6v; and Cα-atom movements for helices E and F from Leu152to Pro186, extracted from pdb entries 6rph minus 6rpq. These movementsin different amplitudes and combinations were used for target moleculardynamics simulations using GROMACS [230].

Influence of the detergent micelle on difference X-rayscattering. In order to model difference X-ray scattering basis spec-tra from heat-corrected TR-XSS data 6.3C,D, we must be able topredict X-ray scattering changes from atomic coordinates for a setsecondary structural changes. The agreement between the theoreti-cal difference scattering prediction and the experimental differencespectra is optimized by sampling various amplitudes and combina-tion of the chosen motions. These candidate motions were simulatedwith a bacteriorhodopsin placed within a detergent micelle of β-octylglucoside, in order to investigate the scattering influences of proteinconformational changes on the micelle and vice-versa, but also thechanges in phase relationships between the X-ray scattering from thedetergent micelle and protein.

Since the protein X-ray scattering contribution from the , or in-tensity is proportional to |F (q)|2, the scattering contribution from the

Figure 6.3: Molecular dynamics simulation of bacteriorhodpsin ina detergent micelle Structure of bR in complex with a detergent micellemade up of 190 molecules of β-octyl glucoside, built using the CHARMM-GUI micelle builder [4].

detergent micelle is about five times larger than that of the protein, asit consists of many more atoms. Furthermore, only parts of the proteinhave their backbone Cα atoms displaced (26%), or approximately 8%of the protein plus the micelle complex, and such makes it challengingto predict the impact that the protein Cα movements have on theX-scattering (figure 6.4). Therefore, a strategy was developed to ad-dress the X-ray scattering changes connected to random fluctuationsof the detergent micelle, so it does not dominate the predicted changes(figure 6.4B-D).

The protocol extracts the influence of the cross-term on the X-raydifference scattering intensity of the protein in complex with the de-tergent micelle, arising from the conformational changes within theprotein alone. Firstly, coordinate files were extracted from the MDsimulations of the protein-micelle complex in it’s resting and modi-fied conformation, and then X-ray scattering predictions were gener-ated using CRYSOL to predict |FPrestMrest(q)|2 and |FPexcMexc(q)|2for each movement of helix C, E and F. To make sure that the ran-dom fluctuations within the detergent micelle cancel, the coordinatesof the detergent micelle was swapped, so that the resting state protein

Figure 6.4: Difference X-ray scattering predictions from MD sim-ulations of bR in complex with detergent micelle consisting of 190β-octyl glucoside molecules. A) Difference X-ray scattering predictionusing CRYSOL [172] from the protein alone as helix C and EF move withdifferent amplitude and combinations, calculated from 2000 snapshots ofa 20 ns trajectory. B-E) Difference X-ray scattering predictions from thesame MD simulation from the micelle alone. B,C,D and E are 500 snap-shots each along the 20 ns trajectory, and show very little correlation tothe regions moving in protein, and displays the random nature of struc-tural changes within the micelle. F) Difference X-ray scattering predictionfrom protein in complex with micelle system. Results from our protocol forinterchanging the detergent micelle between parallel simulations, resultedin difference X-ray scattering changes that evolve smoothly with the move-ment of helix C,E and F.

Figure 6.3: Molecular dynamics simulation of bacteriorhodpsin ina detergent micelle Structure of bR in complex with a detergent micellemade up of 190 molecules of β-octyl glucoside, built using the CHARMM-GUI micelle builder [4].

detergent micelle is about five times larger than that of the protein, asit consists of many more atoms. Furthermore, only parts of the proteinhave their backbone Cα atoms displaced (26%), or approximately 8%of the protein plus the micelle complex, and such makes it challengingto predict the impact that the protein Cα movements have on theX-scattering (figure 6.4). Therefore, a strategy was developed to ad-dress the X-ray scattering changes connected to random fluctuationsof the detergent micelle, so it does not dominate the predicted changes(figure 6.4B-D).

The protocol extracts the influence of the cross-term on the X-raydifference scattering intensity of the protein in complex with the de-tergent micelle, arising from the conformational changes within theprotein alone. Firstly, coordinate files were extracted from the MDsimulations of the protein-micelle complex in it’s resting and modi-fied conformation, and then X-ray scattering predictions were gener-ated using CRYSOL to predict |FPrestMrest(q)|2 and |FPexcMexc(q)|2for each movement of helix C, E and F. To make sure that the ran-dom fluctuations within the detergent micelle cancel, the coordinatesof the detergent micelle was swapped, so that the resting state protein

Figure 6.4: Difference X-ray scattering predictions from MD sim-ulations of bR in complex with detergent micelle consisting of 190β-octyl glucoside molecules. A) Difference X-ray scattering predictionusing CRYSOL [172] from the protein alone as helix C and EF move withdifferent amplitude and combinations, calculated from 2000 snapshots ofa 20 ns trajectory. B-E) Difference X-ray scattering predictions from thesame MD simulation from the micelle alone. B,C,D and E are 500 snap-shots each along the 20 ns trajectory, and show very little correlation tothe regions moving in protein, and displays the random nature of struc-tural changes within the micelle. F) Difference X-ray scattering predictionfrom protein in complex with micelle system. Results from our protocol forinterchanging the detergent micelle between parallel simulations, resultedin difference X-ray scattering changes that evolve smoothly with the move-ment of helix C,E and F.

was placed with the sampled micelle conformation of the excited tra-jectory, and oppositely, the excited conformation of the protein wasplaced within the sampled micelle conformations of the resting trajec-tory, which afforded, |FPrestMexc(q)|2 and |FPexcMrest(q)|2(see figure .This created two complementary sets of pdb files, which were used tocalculate the following expression:

∆|Fpm(q)|2 =12(|FPexcMexc(q)|2) − |FPrestMexc(q)|2

+12(|FPexcMrest(q)|2) − |FPrestMrest(q)|2 (6.1)

This allowed us to make sure that the term proportional the differ-ence X-ray scattering intensity of the detergent micelle is zero. Resid-ual artifacts arising from the micelle packing against the protein, be-ing mis-matched in the complementary pdb files also cancelled out bygiving equal weighting for these two terms. The results yielded fromthese calculations can be seen in figure 6.4,F, showing a smooth con-tinuous progression of ∆|Fpm(q)|2 with the movement of helix C, Eand F. In this way, we managed to create a procedure which allowsthe cross-term between the protein conformational changes and a de-tergent micelle to be evaluated using MD simulations, while avoidingthe central issues the prediction being entirely dominated by randomfluctuations within the detergent micelle. This allows us to recoverthe structural movements of helix C,E and F against the experimen-tal data, with additional corrections for solvent contrast and micellesize.

Influence of detergent micelle size on the difference X-ray scattering. The protein-micelle complex chosen to calculate dif-ference X-ray scattering predictions consisted of 190 β-octyl gluco-side molecules. This number of detergent molecules was chosen asit formed a stable micelle, which completely surrounded the proteinwithout gaps, without excess detergent molecules breaking of duringsimulations. However, the choice was in some measure arbitrary, anddoes not reflect the nature of the experimental sample which would

Figure 6.5: Protocol for cancelling random fluctuations within thedetergent micelle. A) Resting and excited state superimposed, showingstructural differences. B) Snapshot from a MD simulation of the restingstate bR within a detergent micelle. C) Snapshot from a MD simulation ofthe excited state bR within a detergent micelle. D) Creation of a new pdbfile with a snapshot of the protein from a resting state trajectory placedwithin a snapshot of the micelle from the light-activated conformation tra-jectory. E) Creation of a new pdb file with a snapshot of the protein from alight-activated trajectory, placed within a snapshot of the micelle from theresting conformation trajectory.

was placed with the sampled micelle conformation of the excited tra-jectory, and oppositely, the excited conformation of the protein wasplaced within the sampled micelle conformations of the resting trajec-tory, which afforded, |FPrestMexc(q)|2 and |FPexcMrest(q)|2(see figure .This created two complementary sets of pdb files, which were used tocalculate the following expression:

∆|Fpm(q)|2 =12(|FPexcMexc(q)|2) − |FPrestMexc(q)|2

+12(|FPexcMrest(q)|2) − |FPrestMrest(q)|2 (6.1)

This allowed us to make sure that the term proportional the differ-ence X-ray scattering intensity of the detergent micelle is zero. Resid-ual artifacts arising from the micelle packing against the protein, be-ing mis-matched in the complementary pdb files also cancelled out bygiving equal weighting for these two terms. The results yielded fromthese calculations can be seen in figure 6.4,F, showing a smooth con-tinuous progression of ∆|Fpm(q)|2 with the movement of helix C, Eand F. In this way, we managed to create a procedure which allowsthe cross-term between the protein conformational changes and a de-tergent micelle to be evaluated using MD simulations, while avoidingthe central issues the prediction being entirely dominated by randomfluctuations within the detergent micelle. This allows us to recoverthe structural movements of helix C,E and F against the experimen-tal data, with additional corrections for solvent contrast and micellesize.

Influence of detergent micelle size on the difference X-ray scattering. The protein-micelle complex chosen to calculate dif-ference X-ray scattering predictions consisted of 190 β-octyl gluco-side molecules. This number of detergent molecules was chosen asit formed a stable micelle, which completely surrounded the proteinwithout gaps, without excess detergent molecules breaking of duringsimulations. However, the choice was in some measure arbitrary, anddoes not reflect the nature of the experimental sample which would

Figure 6.5: Protocol for cancelling random fluctuations within thedetergent micelle. A) Resting and excited state superimposed, showingstructural differences. B) Snapshot from a MD simulation of the restingstate bR within a detergent micelle. C) Snapshot from a MD simulation ofthe excited state bR within a detergent micelle. D) Creation of a new pdbfile with a snapshot of the protein from a resting state trajectory placedwithin a snapshot of the micelle from the light-activated conformation tra-jectory. E) Creation of a new pdb file with a snapshot of the protein from alight-activated trajectory, placed within a snapshot of the micelle from theresting conformation trajectory.

Figure 6.6: Influence of detergent micelle size on predicted dif-ference X-ray scattering. A) Predicted difference X-ray scattering forprotein-micelle complex with varying detergent micelle sizes. B) The prin-cipal SVD component (blue line), second SVD component (mustard line,scaled by a factor of 100 for comparison) and the principal SVD componentextracted from detergent micelle fluctuations corresponding to the last 5ns of detergent micelle fluctuations alone from MD simulation (6.4E) forcomparison (red line).

contain a distribution of micelle sizes. In order to account for the influ-ence of micelle-size variations, difference X-ray scattering predictionswere repeated from equation 6.1, for a selected movement of helix Eand F but with varying the detergent micelle size from 170-230 β-octylglucoside molecules, in increments of 5 detergent molecules added foreach MD simulation (figure 6.6).

Figure 6.6A shows the predicted difference X-ray scattering forprotein-micelle complex with varying detergent micelle sizes, and ma-jor features of the difference X-ray scattering are consistent for allmicelle sizes. In Figure 6.6B, singular value decomposition (SVD) was

used to extract the mean principal component (blue line), second SVDcomponent (mustard line) from the difference X-ray scattering predic-tions shown in figure 6.6A. Also, worth highlighting is the extractedSVD component of micelle-only fluctuations from figure 5.4E, (redline), which represents the last 5 ns of micelle fluctuations, allowedto equilibrate longer, compared against the second SVD component(mustard line) with high agreement for q ≥ 0.1 Å−1. Thus, if onewants to perform corrections to X-ray scattering data as a result ofeither the micelle fluctuations as seen in the difference X-ray scatter-ing predictions of figure 6.4E, or due to fluctuations in the detergentmicelle cross-term (second SVD component (mustard) in 6.6B) fromequation6.1, the resulting correction are nearly identical for q ≥ 0.1Å−1. This is seemingly due to these corrections to the X-ray scatteringecho the distinct length-scales of the detergent micelle in both cases,and such including both terms, could lead to physically inaccuratedescription, in which the two terms are having large but virtuallycancelling amplitudes. Therefore, only one of these two terms wereincluded in the structural fitting against the experimental data, andas such it was not possible to balance between correction for the fluc-tuations associated with micelle size and those correlated to micellestructural fluctuations.

Correction for the solvent excluded volume. In order to pre-dict the light-induced changes in the difference X-ray scattering fromthe protein-micelle complex, further correction must be due to theinfluence of the solvent-excluded volume. CRYSOL predicts the totalX-ray scattering after correcting for the solvent excluded volume, us-ing empirical corrections for individual atoms [?, 172] which derivedfrom a vast amount of experimental data from soluble proteins. How-ever, a detergent micelle typically has a much lower electron densitythan that of proteins, and it was not clear whether the empirical sol-vent excluded corrections developed for soluble proteins, could be uti-lized for corrections against an integral membrane protein in complexwith a detergent micelle. The protocol developed included runningan MD simulation of a protein-micelle complex within a solvent box

Figure 6.6: Influence of detergent micelle size on predicted dif-ference X-ray scattering. A) Predicted difference X-ray scattering forprotein-micelle complex with varying detergent micelle sizes. B) The prin-cipal SVD component (blue line), second SVD component (mustard line,scaled by a factor of 100 for comparison) and the principal SVD componentextracted from detergent micelle fluctuations corresponding to the last 5ns of detergent micelle fluctuations alone from MD simulation (6.4E) forcomparison (red line).

contain a distribution of micelle sizes. In order to account for the influ-ence of micelle-size variations, difference X-ray scattering predictionswere repeated from equation 6.1, for a selected movement of helix Eand F but with varying the detergent micelle size from 170-230 β-octylglucoside molecules, in increments of 5 detergent molecules added foreach MD simulation (figure 6.6).

Figure 6.6A shows the predicted difference X-ray scattering forprotein-micelle complex with varying detergent micelle sizes, and ma-jor features of the difference X-ray scattering are consistent for allmicelle sizes. In Figure 6.6B, singular value decomposition (SVD) was

used to extract the mean principal component (blue line), second SVDcomponent (mustard line) from the difference X-ray scattering predic-tions shown in figure 6.6A. Also, worth highlighting is the extractedSVD component of micelle-only fluctuations from figure 5.4E, (redline), which represents the last 5 ns of micelle fluctuations, allowedto equilibrate longer, compared against the second SVD component(mustard line) with high agreement for q ≥ 0.1 Å−1. Thus, if onewants to perform corrections to X-ray scattering data as a result ofeither the micelle fluctuations as seen in the difference X-ray scatter-ing predictions of figure 6.4E, or due to fluctuations in the detergentmicelle cross-term (second SVD component (mustard) in 6.6B) fromequation6.1, the resulting correction are nearly identical for q ≥ 0.1Å−1. This is seemingly due to these corrections to the X-ray scatteringecho the distinct length-scales of the detergent micelle in both cases,and such including both terms, could lead to physically inaccuratedescription, in which the two terms are having large but virtuallycancelling amplitudes. Therefore, only one of these two terms wereincluded in the structural fitting against the experimental data, andas such it was not possible to balance between correction for the fluc-tuations associated with micelle size and those correlated to micellestructural fluctuations.

Correction for the solvent excluded volume. In order to pre-dict the light-induced changes in the difference X-ray scattering fromthe protein-micelle complex, further correction must be due to theinfluence of the solvent-excluded volume. CRYSOL predicts the totalX-ray scattering after correcting for the solvent excluded volume, us-ing empirical corrections for individual atoms [?, 172] which derivedfrom a vast amount of experimental data from soluble proteins. How-ever, a detergent micelle typically has a much lower electron densitythan that of proteins, and it was not clear whether the empirical sol-vent excluded corrections developed for soluble proteins, could be uti-lized for corrections against an integral membrane protein in complexwith a detergent micelle. The protocol developed included runningan MD simulation of a protein-micelle complex within a solvent box

Figure 6.7: Corrections for the solvent excluded volume. A) A car-toon representation of the surface calculated from selected water molecules(transparent surface). The protein-micelle complex was carved out as watermolecules and used for solvent exclusion calculations. B) Comparison ofsolvent exclusion correction as a ratio between the total scattering intensityand the scattering intensity of protein-micelle complex when calculated di-rectly for the protein-micelle cooridnates using CRYSOL (blue line) or usingCRYSOL to calculate Fpm(q) from the protein-micelle complex and Fex(q)from the water molecules selected in (A). Plot showing number of watersmolecules selected (blue circles) when using sphere radii from 1 Å to 4 Å.The relative scaling between Fpm(q) and Fex(q) was determined by usinglinear regression to select 3215 water molecules at 0 Å (red line).

filled with water, and then a parallel simulation of the same time-length of a solvent box filled with water alone. Superimposing theprotein-micelle coordinates onto the simulation of with water alone,we selected only the water molecules which were within a sphere of 2Å radius from an atom of the protein or micelle and saved out a pdbfiles of the protein-micelle complex carved out of water (4.4A), whichwere then used to calculate the correction for the solvent excludedvolume for the protein-micelle complex. When cutting out spheres ofwater molecules on all atoms in the protein-micelle complex, therewill be an over-counting of water atoms which protrude beyond theproteins surface. Therefore we compensated for the water selectionby varying the sphere radius between 1 Å to 4 Å, and counting thenumber of water molecules at each radii. This was then used to cre-ate a linear regression to count the number of water molecules at0 Å (see figure 4.4C). Even though the amplitude for the appliedcorrection for |Ftot(q)|2/|Fpm(q)|2 was sensitive to the details of thismethod, both GROMACS and CRYSOL protocols showed very simi-lar shaped correction curves for q ≥ 0.15Å−1 (figure 4.4B), and thusstructural fitting is not affected as such to these details. However,below q = 0.15Å−1, our GROMACS approach anticipates a smallercontrast between the protein and micelle, and as such, the methoddeveloped here does provide low-angle correction.

Structural fitting to the experimental data. Utilizing thetools and protocols described above, we wanted to look for the bestfit to the first and second experimental basis spectra 6.3B by samplingthe amplitude of motions in combination with each other of helix C, Eand F. Structural refinement, as in our earlier research [178,185,189,224], quantified the agreement between theory and experiment usingan R factor,

R =∑ √

(w(q) · (A1 · ∆Itheory(q) − ∆Iexpt(q)))2

∑ √(w(q · ∆Iexpt(q)))2

Figure 6.7: Corrections for the solvent excluded volume. A) A car-toon representation of the surface calculated from selected water molecules(transparent surface). The protein-micelle complex was carved out as watermolecules and used for solvent exclusion calculations. B) Comparison ofsolvent exclusion correction as a ratio between the total scattering intensityand the scattering intensity of protein-micelle complex when calculated di-rectly for the protein-micelle cooridnates using CRYSOL (blue line) or usingCRYSOL to calculate Fpm(q) from the protein-micelle complex and Fex(q)from the water molecules selected in (A). Plot showing number of watersmolecules selected (blue circles) when using sphere radii from 1 Å to 4 Å.The relative scaling between Fpm(q) and Fex(q) was determined by usinglinear regression to select 3215 water molecules at 0 Å (red line).

filled with water, and then a parallel simulation of the same time-length of a solvent box filled with water alone. Superimposing theprotein-micelle coordinates onto the simulation of with water alone,we selected only the water molecules which were within a sphere of 2Å radius from an atom of the protein or micelle and saved out a pdbfiles of the protein-micelle complex carved out of water (4.4A), whichwere then used to calculate the correction for the solvent excludedvolume for the protein-micelle complex. When cutting out spheres ofwater molecules on all atoms in the protein-micelle complex, therewill be an over-counting of water atoms which protrude beyond theproteins surface. Therefore we compensated for the water selectionby varying the sphere radius between 1 Å to 4 Å, and counting thenumber of water molecules at each radii. This was then used to cre-ate a linear regression to count the number of water molecules at0 Å (see figure 4.4C). Even though the amplitude for the appliedcorrection for |Ftot(q)|2/|Fpm(q)|2 was sensitive to the details of thismethod, both GROMACS and CRYSOL protocols showed very simi-lar shaped correction curves for q ≥ 0.15Å−1 (figure 4.4B), and thusstructural fitting is not affected as such to these details. However,below q = 0.15Å−1, our GROMACS approach anticipates a smallercontrast between the protein and micelle, and as such, the methoddeveloped here does provide low-angle correction.

Structural fitting to the experimental data. Utilizing thetools and protocols described above, we wanted to look for the bestfit to the first and second experimental basis spectra 6.3B by samplingthe amplitude of motions in combination with each other of helix C, Eand F. Structural refinement, as in our earlier research [178,185,189,224], quantified the agreement between theory and experiment usingan R factor,

R =∑ √

(w(q) · (A1 · ∆Itheory(q) − ∆Iexpt(q)))2

∑ √(w(q · ∆Iexpt(q)))2

Figure 6.8: Results of structural fitting against difference X-rayscattering data. A) Contour surface showing R-factor for a given struc-tural intermediate from displacement of helices EF and C when fittedagainst state 1 basis spectrum (6.3B, blue line). B) Contour surface show-ing R-factor for a given structural intermediate from displacement of helicesEF and C when fitted against state 2 basis spectrum (6.3B, black line). C)Best-fit difference X-ray scattering prediction (red line) superimposed ontothe experimental data (black circles) for state 1. D) Best-fit difference X-ray scattering prediction (red line) superimposed onto the experimentaldata (black circles) for state 2

Results of the fitting to the experimental using this procedure is il-lustrated in figure 6.8, where minima in the contour surface plot forstate 1 indicate 0.4 ± 0.1 for helix EF and 2 ± 2 for helix C, where theuncertainties are estimated by the contour plot. For state 2, contourplot minima estimated 0.9 ± 0.2 for helix EF and 3 ± 3. Although, thedefined movement of helix C seems to be poorly defined, the actualCα displacement on helix C were only 0.45 Å ± 0.4 Å and 0.3 Å ±0.4 Å for state 1 and 2 respectively.

Comparing this to crystallographic structures 5b6z and 5b6v, the

mean Cα displacement is 0.46 Å for the selected residues of helix C.Likewise, the Cα displacement for residues 152 of helix E to residue182 of helix F were 1.3 Å ± 0.3 Å and 2.9 Å ± 0.6 Å when calculatingthe average Cα coordinates for the sub-set of optimal structures forstate 1 and 2 respectively (see figure . Comparing this to crystallo-graphic structures 6rph and 6rqp, gives a mean Cα displacement of3.4 Å, (after adjusting disordered regions) for the selected residuesof helices E and F. We would argue that the position restraints usedin our target MD simulations which have a force constant of 1000kJ/nm2 on Cα atoms, is not as effective for motions that encountersteric barriers such as the inward motion of helix C, whereas thesebarriers are absent for outwards motions of helix E and F. The result-ing best-fit structures from the protocol developed here can be seenin figure 6.9A.

Summary. TR-XSS studies of integral membrane protein con-formational dynamics have become a mature experimental methodover the last decade [178,185,189,224,231,232]. Despite variations insignal-to-noise ratios, variation in photo-excitation levels, detergentconcentration and other factors associated with sample preparation orexperimental set-up, the measured difference X-ray scattering curvesfrom light-activated integral membrane proteins are highly producible( figure6.3B). Even though structural models have been presentedfrom this body of work, it is the theoretical foundations underliningthe modelling of XSS data from integral membrane proteins whichhas been explicitly addressed here. This has provided a frameworkallowing an atomistic description of the protein and detergent mi-celle by using the CHARMM-GUI micelle builder [4] and the molec-ular dynamics package GROMACS [230] together with theoreticalscattering prediction from atomic coordinates using CRYSOL [172].The first step as identifying candidate structural intermediates of sec-ondary structural motion, which in this analysis was retrieved fromtime-resolved serial crystallography studies of bR [36,43]. Using thesestructural intermediates we could guide the simulations to a targetstructure, and from this identify how the random fluctuations withinthe detergent micelle influence difference X-ray scattering predictions

Figure 6.8: Results of structural fitting against difference X-rayscattering data. A) Contour surface showing R-factor for a given struc-tural intermediate from displacement of helices EF and C when fittedagainst state 1 basis spectrum (6.3B, blue line). B) Contour surface show-ing R-factor for a given structural intermediate from displacement of helicesEF and C when fitted against state 2 basis spectrum (6.3B, black line). C)Best-fit difference X-ray scattering prediction (red line) superimposed ontothe experimental data (black circles) for state 1. D) Best-fit difference X-ray scattering prediction (red line) superimposed onto the experimentaldata (black circles) for state 2

Results of the fitting to the experimental using this procedure is il-lustrated in figure 6.8, where minima in the contour surface plot forstate 1 indicate 0.4 ± 0.1 for helix EF and 2 ± 2 for helix C, where theuncertainties are estimated by the contour plot. For state 2, contourplot minima estimated 0.9 ± 0.2 for helix EF and 3 ± 3. Although, thedefined movement of helix C seems to be poorly defined, the actualCα displacement on helix C were only 0.45 Å ± 0.4 Å and 0.3 Å ±0.4 Å for state 1 and 2 respectively.

Comparing this to crystallographic structures 5b6z and 5b6v, the

mean Cα displacement is 0.46 Å for the selected residues of helix C.Likewise, the Cα displacement for residues 152 of helix E to residue182 of helix F were 1.3 Å ± 0.3 Å and 2.9 Å ± 0.6 Å when calculatingthe average Cα coordinates for the sub-set of optimal structures forstate 1 and 2 respectively (see figure . Comparing this to crystallo-graphic structures 6rph and 6rqp, gives a mean Cα displacement of3.4 Å, (after adjusting disordered regions) for the selected residuesof helices E and F. We would argue that the position restraints usedin our target MD simulations which have a force constant of 1000kJ/nm2 on Cα atoms, is not as effective for motions that encountersteric barriers such as the inward motion of helix C, whereas thesebarriers are absent for outwards motions of helix E and F. The result-ing best-fit structures from the protocol developed here can be seenin figure 6.9A.

Summary. TR-XSS studies of integral membrane protein con-formational dynamics have become a mature experimental methodover the last decade [178,185,189,224,231,232]. Despite variations insignal-to-noise ratios, variation in photo-excitation levels, detergentconcentration and other factors associated with sample preparation orexperimental set-up, the measured difference X-ray scattering curvesfrom light-activated integral membrane proteins are highly producible( figure6.3B). Even though structural models have been presentedfrom this body of work, it is the theoretical foundations underliningthe modelling of XSS data from integral membrane proteins whichhas been explicitly addressed here. This has provided a frameworkallowing an atomistic description of the protein and detergent mi-celle by using the CHARMM-GUI micelle builder [4] and the molec-ular dynamics package GROMACS [230] together with theoreticalscattering prediction from atomic coordinates using CRYSOL [172].The first step as identifying candidate structural intermediates of sec-ondary structural motion, which in this analysis was retrieved fromtime-resolved serial crystallography studies of bR [36,43]. Using thesestructural intermediates we could guide the simulations to a targetstructure, and from this identify how the random fluctuations withinthe detergent micelle influence difference X-ray scattering predictions

Figure 6.9: Light-induced conformational changes in bR predictedfrom structural modelling against difference X-ray scattering ba-sis spectra. A) Structural results from the fitting protocol developed inthis work. Resting state of bR is illustrated in purple; the best-fit structurefor State 1 is illustrated in pink; and best-fit structure for State 2 is illus-trated in grey/blue. B) Structural results from fitting protocol developedby Andersson et al. [178]. The bR resting structure is illustrated in purple;the best-fit structure for the intermediate conformation state as describedin [178] in pink; and the best-fit structure for the late conformation state asdescribed by [178] in grey/blue. C) Light-induced Cα-atom displacement ofthe two modelling protocols. The modelling protocol developed in this workshow the Cα-atom displacement amplitudes against the residue number forState 1 (blue) and State 2 (purple); and the intermediate (mustard) andlate conformation (red) of reference [178].

6.4B-E, and how detergent micelle size influenced the difference X-ray scattering 6.6. A crucial conceptual advance was the exchangingof atomic coordinates of the protein-micelle complex when comparingMD simulations of resting and light-activated structures 6.5C-F, al-lowing us to establish a protocol in which the protein-micelle X-rayscattering cross-term could be determined without being dominatedby random fluctuations within the detergent micelle. Solvent contrastcorrections were also made, but did not affect overall structural fit-ting, but provided rather a correction below q = 0.15 Å−1. The meanCα displacement for helix C was found to be 0.45 Å ± 0.4 Å and 0.3Å ± 0.4 Å for state 1 and 2 respectively, and for the movement ofthe cytoplasmic portions of helix E and F we recovered a mean Cαdisplacement of 1.3 Å ± 0.3 Å and 2.9 Å ± 0.6 Å for state 1 and 2respectively. This work underpins earlier assumptions when workingwith integral membrane proteins [178,185,189,224,231,232], that thepresence of detergent micelles does not necessarily affect structuralmodelling focused on data in the domain q ≥ 0.2Å−1. Furthermore,this analysis improves the fit to low scattering angle data 6.4E,F, andproves the assumption that this region is dominated by the influenceof the detergent micelle. With the development of microfluidic mixing,caged compounds [232] technologies for the initiation of reactions, itshould be possible to extend the field of XSS beyond light-activatedproteins. The analysis tools and protocols developed here should pro-vide a framework for the future advancement of the field and proteindynamics of integral membrane proteins.

Figure 6.9: Light-induced conformational changes in bR predictedfrom structural modelling against difference X-ray scattering ba-sis spectra. A) Structural results from the fitting protocol developed inthis work. Resting state of bR is illustrated in purple; the best-fit structurefor State 1 is illustrated in pink; and best-fit structure for State 2 is illus-trated in grey/blue. B) Structural results from fitting protocol developedby Andersson et al. [178]. The bR resting structure is illustrated in purple;the best-fit structure for the intermediate conformation state as describedin [178] in pink; and the best-fit structure for the late conformation state asdescribed by [178] in grey/blue. C) Light-induced Cα-atom displacement ofthe two modelling protocols. The modelling protocol developed in this workshow the Cα-atom displacement amplitudes against the residue number forState 1 (blue) and State 2 (purple); and the intermediate (mustard) andlate conformation (red) of reference [178].

6.4B-E, and how detergent micelle size influenced the difference X-ray scattering 6.6. A crucial conceptual advance was the exchangingof atomic coordinates of the protein-micelle complex when comparingMD simulations of resting and light-activated structures 6.5C-F, al-lowing us to establish a protocol in which the protein-micelle X-rayscattering cross-term could be determined without being dominatedby random fluctuations within the detergent micelle. Solvent contrastcorrections were also made, but did not affect overall structural fit-ting, but provided rather a correction below q = 0.15 Å−1. The meanCα displacement for helix C was found to be 0.45 Å ± 0.4 Å and 0.3Å ± 0.4 Å for state 1 and 2 respectively, and for the movement ofthe cytoplasmic portions of helix E and F we recovered a mean Cαdisplacement of 1.3 Å ± 0.3 Å and 2.9 Å ± 0.6 Å for state 1 and 2respectively. This work underpins earlier assumptions when workingwith integral membrane proteins [178,185,189,224,231,232], that thepresence of detergent micelles does not necessarily affect structuralmodelling focused on data in the domain q ≥ 0.2Å−1. Furthermore,this analysis improves the fit to low scattering angle data 6.4E,F, andproves the assumption that this region is dominated by the influenceof the detergent micelle. With the development of microfluidic mixing,caged compounds [232] technologies for the initiation of reactions, itshould be possible to extend the field of XSS beyond light-activatedproteins. The analysis tools and protocols developed here should pro-vide a framework for the future advancement of the field and proteindynamics of integral membrane proteins.

6.2 Time-resolved X-ray Solution ScatteringObservations of Light-Induced StructuralChanges in Sensory Rhodopsin II (PA-PER II)

The ability to receive and respond to chemical and physical signal iscrucial for all living organisms. The simplest signalling cascades can befound in unicellular organisms, and provides a model for explaining thefundamental principles of signalling across all life on earth. SRII is ablue-light receptor which controls the cell’s swimming behaviour in thehost haloarchea, allowing the directed movement away from harmfullight as a response to changes in light intensity and color. In this work,we applied TR-XSS to observe light-driven structural changes in thesensory receptor SRII from Natronobacterium pharaonis in isolationand in complex with it’s transducer protein (HtrII).

Time-resolved difference X-ray scattering data. Figure 6.10A,shows the experimental difference X-ray scattering curves recordedfrom SRII in isolation after rejecting outliers, merging data from runsand removing the heat contribution. These curves show oscillationsin ∆S(q, ∆t) with a local minimum shown at q ≈ 0.22 Å−1 which isconsistent with our previous work from Andersson et al. [178]. Localmaxima are also shown at q ≈ 0.15 Å−1,q ≈ 0.33 Å−1 and q ≈ 0.55Å−1and are consistent throughout the data until the last time-point∆t = 20 ms, albeit with lower amplitude for the earliest time-point∆t = 2.5 µs. We can observe that the amplitude for the differencesignal ∆S(q, ∆t) is reduced for ∆t = 20 ms, which reflects the sam-ple flowing out of the X-ray beam spot, probing the sample, movingapproximately 200 µm in 20 ms, rather than being indicative of de-caying protein conformational change. The photo-cycle of SRII candepending on solubilization conditions [233] be extended for up to 2s, and there is no evidence to suggest that decay of conformationalchanges would more rapidly.

In figure 6.10B, we can see the TR-XSS data for the SRII:HtrIIcomplex, with same local minima as SRII at q ≈ 0.22 Å−1 throughout

6.2. Time-resolved X-ray Solution Scattering Observations ofLight-Induced Structural Changes in Sensory Rhodopsin II (PAPERII)

Figure 6.10: Time-resolved difference X-ray scattering data ofSRII and SRII:HtrII complex, following photo-activation. A-B)Time-dependant changes in the difference X-ray scattering, correspondingwith SRII in isolation and SRII:HtrII complex from left to right. Blackline shows the experimental data and red line shows the data reconstructedfrom the basis spectra in panel E & F respectively. C-D) Time-evolutionof spectral changes correlated with the growth and decay of distinct statesin SRII and SRII:HtrII respectively. E-F) Basis spectra showing state 1and 2 (top and bottom respectively) of SRII and SRII:HtrII from lineardecomposition respectively. 75

6.2 Time-resolved X-ray Solution ScatteringObservations of Light-Induced StructuralChanges in Sensory Rhodopsin II (PA-PER II)

The ability to receive and respond to chemical and physical signal iscrucial for all living organisms. The simplest signalling cascades can befound in unicellular organisms, and provides a model for explaining thefundamental principles of signalling across all life on earth. SRII is ablue-light receptor which controls the cell’s swimming behaviour in thehost haloarchea, allowing the directed movement away from harmfullight as a response to changes in light intensity and color. In this work,we applied TR-XSS to observe light-driven structural changes in thesensory receptor SRII from Natronobacterium pharaonis in isolationand in complex with it’s transducer protein (HtrII).

Time-resolved difference X-ray scattering data. Figure 6.10A,shows the experimental difference X-ray scattering curves recordedfrom SRII in isolation after rejecting outliers, merging data from runsand removing the heat contribution. These curves show oscillationsin ∆S(q, ∆t) with a local minimum shown at q ≈ 0.22 Å−1 which isconsistent with our previous work from Andersson et al. [178]. Localmaxima are also shown at q ≈ 0.15 Å−1,q ≈ 0.33 Å−1 and q ≈ 0.55Å−1and are consistent throughout the data until the last time-point∆t = 20 ms, albeit with lower amplitude for the earliest time-point∆t = 2.5 µs. We can observe that the amplitude for the differencesignal ∆S(q, ∆t) is reduced for ∆t = 20 ms, which reflects the sam-ple flowing out of the X-ray beam spot, probing the sample, movingapproximately 200 µm in 20 ms, rather than being indicative of de-caying protein conformational change. The photo-cycle of SRII candepending on solubilization conditions [233] be extended for up to 2s, and there is no evidence to suggest that decay of conformationalchanges would more rapidly.

In figure 6.10B, we can see the TR-XSS data for the SRII:HtrIIcomplex, with same local minima as SRII at q ≈ 0.22 Å−1 throughout

Figure 6.10: Time-resolved difference X-ray scattering data ofSRII and SRII:HtrII complex, following photo-activation. A-B)Time-dependant changes in the difference X-ray scattering, correspondingwith SRII in isolation and SRII:HtrII complex from left to right. Blackline shows the experimental data and red line shows the data reconstructedfrom the basis spectra in panel E & F respectively. C-D) Time-evolutionof spectral changes correlated with the growth and decay of distinct statesin SRII and SRII:HtrII respectively. E-F) Basis spectra showing state 1and 2 (top and bottom respectively) of SRII and SRII:HtrII from lineardecomposition respectively. 75

Figure 6.11: Comparison of basis spectra extracted from lineardecomposition A) Superimposed basis spectra of SRII state 1 and 2 inblue (top and bottom) and basis spectra of SRII:HtrII state 1 and 2 inorange (top and bottom) respectively. B) Overlay of SRII state 2 (blue)and the late conformation of bR (purple). For q ≥ 0.4Å−1, the differenceX-ray scattering between the two are very similar.

all time-points. The early time-points ∆t = 2.5 µs and ∆t = 6.3µs are very similar shape of ∆S(q, ∆t) as the early difference X-rayscattering changes observed for SRII in isolation. Along the time-evolution of the difference curves, the local maxima at q ≈ 0.33 Å−1

dampens, and becomes an oscillation of smaller amplitude centered atq ≈ 0.30 Å−1, and in parallel, a positive peak centered at q ≈ 0.60 Å−1

increases in amplitude to become rather distinct. Thus, comparingonly the difference X-ray scattering curves data, we can see that thepresence of the transducer complex indeed influences the recordedX-ray scattering profile in a fashion that can be measured withoutambiguity.

Linear decomposition of the difference X-ray scatteringdata. In order to evaluate whether it was necessary to describe therecorded data with more than one component, linear decompositionwas applied to the TR-XSS data. As can be seen in 6.10C,D, in thismethod, the populations of each of the two components (State 1 andState 2) are modeled with an exponential decay, where State 1 initiallyhaving a population of 1, reflecting the presence of a structural signalin the earliest time-point, and then decaying exponentially with a timeconstant τ1 with a parallel increase in the population of State 2, mod-eled as decaying to zero with time-constant τ2. Figures 6.10C,D dis-play these populations for States 1 and 2 resulting from linear decom-position of SRII and SRII:HtrII data, with very similar time-constantsτ1 (τ1 = 6.3 ± 0.1 µs and τ1 = 6.0 ± 0.1 µs respectively. Furthermore,the basis spectra for State 1 for both SRIIS1 and SRII:HtrIIS2 are in-distinguishable within signal-to-noise (6.11,A), whereas State 2 basisspectra are quite distinct as seen in figure 6.11A. This would stronglysuggest that initially, the structural changes within SRII are identicalregardless of the transducer being bound or not, but rather the effectof bound transducer protein affects the time-evolution of X-ray scat-tering changes. The second component decays with a time-constant ofτ2 = 620 ± 2 ms for SRII and τ2 = 33 ± 3 ms for SRII:HtrII. Again, asmentioned earlier, the discrepancy in τ2 between these two samples re-flects the time it takes to sweep the light-activated sample away fromthe from the X-ray probed volume, and replaced with protein samplethat haven’t been pumped by laser light, and as such, these differ-ences show simply as a result of different flow-rates and experimentalgeometries in the two experiments.

Structural modelling of X-ray scattering changes. The dif-ference X-ray scattering curves of recorded from SRIIS2 strong agree-ment with earlier TR-XSS data recorded from bR, specifically forq ≥ 0.41Å−1, where the SRIIS2 and late conformation of bR are al-most identical (figure 6.11B), which suggests that there are similarlight-induced structural changes that occur in SRII and bR. The lowangle differences between the two systems relates to how the deter-gent micelle packs around these two proteins [234], so even if both

Figure 6.11: Comparison of basis spectra extracted from lineardecomposition A) Superimposed basis spectra of SRII state 1 and 2 inblue (top and bottom) and basis spectra of SRII:HtrII state 1 and 2 inorange (top and bottom) respectively. B) Overlay of SRII state 2 (blue)and the late conformation of bR (purple). For q ≥ 0.4Å−1, the differenceX-ray scattering between the two are very similar.

all time-points. The early time-points ∆t = 2.5 µs and ∆t = 6.3µs are very similar shape of ∆S(q, ∆t) as the early difference X-rayscattering changes observed for SRII in isolation. Along the time-evolution of the difference curves, the local maxima at q ≈ 0.33 Å−1

dampens, and becomes an oscillation of smaller amplitude centered atq ≈ 0.30 Å−1, and in parallel, a positive peak centered at q ≈ 0.60 Å−1

increases in amplitude to become rather distinct. Thus, comparingonly the difference X-ray scattering curves data, we can see that thepresence of the transducer complex indeed influences the recordedX-ray scattering profile in a fashion that can be measured withoutambiguity.

Linear decomposition of the difference X-ray scatteringdata. In order to evaluate whether it was necessary to describe therecorded data with more than one component, linear decompositionwas applied to the TR-XSS data. As can be seen in 6.10C,D, in thismethod, the populations of each of the two components (State 1 andState 2) are modeled with an exponential decay, where State 1 initiallyhaving a population of 1, reflecting the presence of a structural signalin the earliest time-point, and then decaying exponentially with a timeconstant τ1 with a parallel increase in the population of State 2, mod-eled as decaying to zero with time-constant τ2. Figures 6.10C,D dis-play these populations for States 1 and 2 resulting from linear decom-position of SRII and SRII:HtrII data, with very similar time-constantsτ1 (τ1 = 6.3 ± 0.1 µs and τ1 = 6.0 ± 0.1 µs respectively. Furthermore,the basis spectra for State 1 for both SRIIS1 and SRII:HtrIIS2 are in-distinguishable within signal-to-noise (6.11,A), whereas State 2 basisspectra are quite distinct as seen in figure 6.11A. This would stronglysuggest that initially, the structural changes within SRII are identicalregardless of the transducer being bound or not, but rather the effectof bound transducer protein affects the time-evolution of X-ray scat-tering changes. The second component decays with a time-constant ofτ2 = 620 ± 2 ms for SRII and τ2 = 33 ± 3 ms for SRII:HtrII. Again, asmentioned earlier, the discrepancy in τ2 between these two samples re-flects the time it takes to sweep the light-activated sample away fromthe from the X-ray probed volume, and replaced with protein samplethat haven’t been pumped by laser light, and as such, these differ-ences show simply as a result of different flow-rates and experimentalgeometries in the two experiments.

Structural modelling of X-ray scattering changes. The dif-ference X-ray scattering curves of recorded from SRIIS2 strong agree-ment with earlier TR-XSS data recorded from bR, specifically forq ≥ 0.41Å−1, where the SRIIS2 and late conformation of bR are al-most identical (figure 6.11B), which suggests that there are similarlight-induced structural changes that occur in SRII and bR. The lowangle differences between the two systems relates to how the deter-gent micelle packs around these two proteins [234], so even if both

Figure 6.12: Results from structural modelling of light-drivenstructural changes in SRII. A) State 2 basis spectrum extracted fromTR-XSS data (black) recorded from SRIIS2 in isolation, and the best-fitmodel (red superimposed). B) Contour surface showing R-factor for a givenstructural displacement during fitting of SRIIS2, using protocol developedin [234]. C) State 1 basis spectrum extracted from TR-XSS data (black)recorded from SRII:HtrIIS1 and the best-fit model (red) superimposed. D)Contour surface showing R-factor for a given structural displacement duringfitting of SRII:HtrIIS1 using protocol developed in [234]. E) State 2 basisspectrum extracted from TR-XSS data (black) recorded from SRII:HtrIIS2and the best-fit model (red) superimposed. F) Contour surface showingR-factor for a given structural displacement during fitting of SRII:HtrIIS2using protocol developed in [234].

systems used β-octylglucoside as detergent, different pH, concentra-tion of counter ions and other factors combined, seem to influencethe difference X-ray scattering at low angles. We model the observedchanges in X-ray scattering, ∆S(q, ∆t), and extracted the linearlydecomposed basis-spectra of SRIIS2, SRII:HtrIIS1 and SRII:HtrIIS2,using the protocol developed in [234]. The selected secondary struc-tures chosen to model the light-activated model for SRII, were helicesC,D,E and F, which has been observed to change in time-resolvedserial synchrotron crystallography (TR-SSX) of SRII [235]. ResiduesPhe69 to Leu82 on the extracellular side of helix C together withresidues Val107 to Phe127 also on the extracelluular side of helicesD and E were combined as one candidate movement; and residuesPro144 to Pro175 of helices E and F were chosen as a second candi-date motion. Target structural intermediates were created by scalingthese movements associated with the aforementioned residues by thefactor γ for the combined movement of helix C,D and E; and scalingby the factor δ for the movement of helices E and F.

Figure 6.12, shows the results of structural fitting against the ex-perimental data and the best-fits for all three basis spectra of SRIIS2,SRII:HtrIIS1 and SRII:HtrIIS2, and also the contour surfaces recov-ered as γ and δ are varied. The height in the contoured surface re-lates to the agreement between the theoretical and experimental data,which is commonly referred to as the R-factor [234]. From the best-fitcurves (figure 6.12A,C,D), it can be seen that all theoretical modelscapture the main difference featured of the experimental data, evenat the X-ray scattering minimum of q = 0.22Å−1, which correlateshighly influenced by the detergent micelle [234]. The best-fit weightedR-factors and Pearson correlation coefficients calculated for q ≥ 0.3Å−1 were R = 32%, P = 94% for SRIIS2; R = 61%, P = 83% forSRII:HtrIIS1; and R = 26%, P = 97% for SRII:HtrIIS2. The contoursurfaces in 6.12B,D,F illustrate that structural modelling was betterat resolving the amplitude of motions associated with helices E and F,where as the R-factor was not as sensitive to changes in the amplitudeof motions correlated with the extracellular domains of helices C,Dand E. Extracting and averaging the conformations from molecular

Figure 6.12: Results from structural modelling of light-drivenstructural changes in SRII. A) State 2 basis spectrum extracted fromTR-XSS data (black) recorded from SRIIS2 in isolation, and the best-fitmodel (red superimposed). B) Contour surface showing R-factor for a givenstructural displacement during fitting of SRIIS2, using protocol developedin [234]. C) State 1 basis spectrum extracted from TR-XSS data (black)recorded from SRII:HtrIIS1 and the best-fit model (red) superimposed. D)Contour surface showing R-factor for a given structural displacement duringfitting of SRII:HtrIIS1 using protocol developed in [234]. E) State 2 basisspectrum extracted from TR-XSS data (black) recorded from SRII:HtrIIS2and the best-fit model (red) superimposed. F) Contour surface showingR-factor for a given structural displacement during fitting of SRII:HtrIIS2using protocol developed in [234].

systems used β-octylglucoside as detergent, different pH, concentra-tion of counter ions and other factors combined, seem to influencethe difference X-ray scattering at low angles. We model the observedchanges in X-ray scattering, ∆S(q, ∆t), and extracted the linearlydecomposed basis-spectra of SRIIS2, SRII:HtrIIS1 and SRII:HtrIIS2,using the protocol developed in [234]. The selected secondary struc-tures chosen to model the light-activated model for SRII, were helicesC,D,E and F, which has been observed to change in time-resolvedserial synchrotron crystallography (TR-SSX) of SRII [235]. ResiduesPhe69 to Leu82 on the extracellular side of helix C together withresidues Val107 to Phe127 also on the extracelluular side of helicesD and E were combined as one candidate movement; and residuesPro144 to Pro175 of helices E and F were chosen as a second candi-date motion. Target structural intermediates were created by scalingthese movements associated with the aforementioned residues by thefactor γ for the combined movement of helix C,D and E; and scalingby the factor δ for the movement of helices E and F.

Figure 6.12, shows the results of structural fitting against the ex-perimental data and the best-fits for all three basis spectra of SRIIS2,SRII:HtrIIS1 and SRII:HtrIIS2, and also the contour surfaces recov-ered as γ and δ are varied. The height in the contoured surface re-lates to the agreement between the theoretical and experimental data,which is commonly referred to as the R-factor [234]. From the best-fitcurves (figure 6.12A,C,D), it can be seen that all theoretical modelscapture the main difference featured of the experimental data, evenat the X-ray scattering minimum of q = 0.22Å−1, which correlateshighly influenced by the detergent micelle [234]. The best-fit weightedR-factors and Pearson correlation coefficients calculated for q ≥ 0.3Å−1 were R = 32%, P = 94% for SRIIS2; R = 61%, P = 83% forSRII:HtrIIS1; and R = 26%, P = 97% for SRII:HtrIIS2. The contoursurfaces in 6.12B,D,F illustrate that structural modelling was betterat resolving the amplitude of motions associated with helices E and F,where as the R-factor was not as sensitive to changes in the amplitudeof motions correlated with the extracellular domains of helices C,Dand E. Extracting and averaging the conformations from molecular

dynamics for the selected values of γ and δ we can identify the move-ments to a mean displacement of Cα-atoms from Pro144 to Pro175 of1.7 Å ± 0.4 Å for SRIIS2, 0.8 Å ± 0.2 Å for SRII:HtrIIS1, and 1.3 Å ±0.3 Å for SRII:HtrIIS2. Crystallographic from TR-SSX data captured30 ms after photoactivation im comparison show a mean displacementof 0.92 Å in this region. The mean Cα displacement of Phe69 to Leu82on the extracellular side of helix C were 0.3 Å ± 0.2 Å for SRIIS2, 0.3Å ± 0.2 Å for SRII:HtrIIS1 and 0.4 Å ± 0.3 Å for SRII:HtrIIS2 whichin all cases are lower than the value 0.75 Å captured the TR-SSXdata 30 ms structure. Lastly, the coupled movement of extracellularportions of helices D and E from Val107 to Phe127 calculated Cα dis-placements of 0.3 Å ± 0.2 Å for SRIIS2, 0.4 Å ± 0.2 Å for SRII:HtrIIS1and 0.6 Å ± 0.4 Å for SRII:HtrIIS2, that are all lower than the valueof 0.89 Å of displacement calculated from TR-SSX 30 ms data. Infigure 6.13, the displacement of each SRII in isolation and SRII:HtrIIcomplex is illustrated as averaged best-fit structure. Our fitting of theTR-XSS data recovered from SRII in isolation and in complex withit’s transducer reveal an outward movement of the cytoplasmic por-tions of helices E and F that is smaller in the presence of transducer.This movement is still smaller than the EF movement observed in bRbut larger than observed serial crystallography. Our data is also con-sistent with motions of the extracellular portions of helices C,D and Ebut with smaller amplitudes than that recovered in the TR-SSX, andnot much larger than equilibrium fluctuations occurring throughoutthe protein (figure 6.13E), and as such the recovered values carry alarger relative uncertainty.

Summary. Structural details concerning light-induced signallingin unicellular organisms like archaea can provide fundamental insightinto the mechanism of sensory signalling in across all living organisms,by elucidating it’s underlying principles. Our data show that the ba-sis spectra of SRIIS1 and SRII:HtrIIS1 are identical within signal tonoise, suggesting that the initial structural trajectory of SRII is notaffected by the presence or absence of transducer protein. However,as time evolves, the SRIIS2 and SRII:HtrIIS2 basis spectra divergeand our structural modelling concludes that SRII undergoes a 30%

Figure 6.13: Illustration of light-activated structural changes inSRII in isolation and the SRII:HtrII complex. A-B) Superposi-tion of the SRII resting model (gray) and the results from fitting SRIIS2,viewed from the membrane plane and perpendicular to the plane of themembrane from the cytoplasmic side respectively. C-D Superposition ofthe SRII resting model (gray) and the results from fitting SRII:HtrIIS2,viewed from the membrane plane and perpendicular to the plane of themembrane from the cytoplasmic side respectively. E) Root mean squaredisplacements of Cα-atoms resulting from sampling MD simulations withbest-fit to the difference X-ray scattering data. Blue: mean displacement ofCα-atoms when fitting SRIIS2. Red:mean displacement of Cα-atoms whenfitting SRII:HtrIIS1. Mustard: mean displacement of Cα-atoms when fit-ting SRII:HtrIIS2. In this plot SRII is from residue 1-219 and HtrII is from220-285.

dynamics for the selected values of γ and δ we can identify the move-ments to a mean displacement of Cα-atoms from Pro144 to Pro175 of1.7 Å ± 0.4 Å for SRIIS2, 0.8 Å ± 0.2 Å for SRII:HtrIIS1, and 1.3 Å ±0.3 Å for SRII:HtrIIS2. Crystallographic from TR-SSX data captured30 ms after photoactivation im comparison show a mean displacementof 0.92 Å in this region. The mean Cα displacement of Phe69 to Leu82on the extracellular side of helix C were 0.3 Å ± 0.2 Å for SRIIS2, 0.3Å ± 0.2 Å for SRII:HtrIIS1 and 0.4 Å ± 0.3 Å for SRII:HtrIIS2 whichin all cases are lower than the value 0.75 Å captured the TR-SSXdata 30 ms structure. Lastly, the coupled movement of extracellularportions of helices D and E from Val107 to Phe127 calculated Cα dis-placements of 0.3 Å ± 0.2 Å for SRIIS2, 0.4 Å ± 0.2 Å for SRII:HtrIIS1and 0.6 Å ± 0.4 Å for SRII:HtrIIS2, that are all lower than the valueof 0.89 Å of displacement calculated from TR-SSX 30 ms data. Infigure 6.13, the displacement of each SRII in isolation and SRII:HtrIIcomplex is illustrated as averaged best-fit structure. Our fitting of theTR-XSS data recovered from SRII in isolation and in complex withit’s transducer reveal an outward movement of the cytoplasmic por-tions of helices E and F that is smaller in the presence of transducer.This movement is still smaller than the EF movement observed in bRbut larger than observed serial crystallography. Our data is also con-sistent with motions of the extracellular portions of helices C,D and Ebut with smaller amplitudes than that recovered in the TR-SSX, andnot much larger than equilibrium fluctuations occurring throughoutthe protein (figure 6.13E), and as such the recovered values carry alarger relative uncertainty.

Summary. Structural details concerning light-induced signallingin unicellular organisms like archaea can provide fundamental insightinto the mechanism of sensory signalling in across all living organisms,by elucidating it’s underlying principles. Our data show that the ba-sis spectra of SRIIS1 and SRII:HtrIIS1 are identical within signal tonoise, suggesting that the initial structural trajectory of SRII is notaffected by the presence or absence of transducer protein. However,as time evolves, the SRIIS2 and SRII:HtrIIS2 basis spectra divergeand our structural modelling concludes that SRII undergoes a 30%

Figure 6.13: Illustration of light-activated structural changes inSRII in isolation and the SRII:HtrII complex. A-B) Superposi-tion of the SRII resting model (gray) and the results from fitting SRIIS2,viewed from the membrane plane and perpendicular to the plane of themembrane from the cytoplasmic side respectively. C-D Superposition ofthe SRII resting model (gray) and the results from fitting SRII:HtrIIS2,viewed from the membrane plane and perpendicular to the plane of themembrane from the cytoplasmic side respectively. E) Root mean squaredisplacements of Cα-atoms resulting from sampling MD simulations withbest-fit to the difference X-ray scattering data. Blue: mean displacement ofCα-atoms when fitting SRIIS2. Red:mean displacement of Cα-atoms whenfitting SRII:HtrIIS1. Mustard: mean displacement of Cα-atoms when fit-ting SRII:HtrIIS2. In this plot SRII is from residue 1-219 and HtrII is from220-285.

larger movement of the cytoplasmic portion of helices E and F whenin isolation, relative to SRII in complex with HtrII (figure 6.12, 6.13).This observation may explain why proton conduction in the cytoplas-mic portion of SRII is blocked by the presence of HrII [236]. Relateddisplacements have been observed of helix F in bR and SRII:HtrII us-ing spin-labeled EPR biophysical methods [237, 238], where the am-plitude of light-activated outward movement of helix F in both bRand SRII being described to be at minimum 1 Å [237], which is con-sistent with our modelling of TR-XSS data. Cryo-trapping of light-activated structural changes correlated with the M-state of SRII inisolation gave a mean Cα displacement from Pro144 to Pro175 of 0.75Å (PDB entry 3qdc [239]). Trapping studies of SRII:HtrII yieldedeven smaller Cα displacement of 0.34 Å for the same residues (PDBentry 2f95 [240]), and 0.47 Å (PDB entry [65]). Temperature TR-SSX study of SRII in isolation showed a mean Cα displacement of thesame residues to be 0.92 Å, which is larger than cryo-trapping studies,but nevertheless smaller amplitudes recovered by our analysis. Thissuggests that thaw/freeze protocols used during illumination of crys-tals of SRII:HtrII does not successfully capture the full extent of theproteins motion. This has also been observed in many cryo-trappingstudies of bR as reviewed by Wickstrand et al. [27].

With the recent improvements in structural modelling against TR-XSS data of integral membrane proteins [234], it seems that a largersampling of MD trajectories corresponding to SRIIS2 and SRII:HtrIIS2best fits may provide more detailed structural insights into signalpropagation between SRII and the HtrII transducer protein. Since,TR-XSS does not depend on crystal formation, one may reproducethese studies on a more full length transducer protein [241]. The ex-perimental and analytical application that we developed here can beextended to other biophysical studies [237,241] as a strong structuralcomplement to study the mechanism of signal propagation in haloar-chaea.

6.3. Time-resolved X-ray Solution Scattering Studies of StructuralChanges in Channelrhodopsin 2 (PAPER III)

6.3 Time-resolved X-ray Solution ScatteringStudies of Structural Changes in Chan-nelrhodopsin 2 (PAPER III)

Channelrhodopsins (ChRs) are light gated ion channels that havebecome important model proteins in neuroscience and optogenetics[83, 242] , whereby light is used to depolarize neuronal membranes.Today ChRs and related proteins are used to study behaviours andlearning in animals from flies [243,244], to non-human primates [245].Channelrhodopsin-2 (ChR2) from the green alga Chlamydomonas rein-hardtii, are in nature involved in regulation of phototaxis, allowingunicellular alga to swim towards optimal light conditions [246].

Although the resting state conformation of ChR2 has been solvedto high resolution [89], questions remain regarding to what degreethe magnitude of conformational changes play, for the mechanism ofchannel opening and closing. Here we applied TR-XSS to probe thelight-induced structural changes in ChR2, both in the presence andabsence of Ca2+, it’s primary transported substrate.

Linear decomposition of the difference X-ray scatteringdata. Linear decomposition of the TR-XSS data were performed aspreviously described [178]. The procedure, uses a kinetic model fora sequence of two or three intermediate states, whereby the rate-constants are optimized for the best agreement between experimentaldata (black, figure 6.15A,B) and the sum of the basis spectra ex-tracted from linear-decomposition given the amplitudes predicted bythe kinetic model (red, figure 6.15A,B). In order to get a reasonablereconstruction of the TR-XSS data of ChR2-N24Q without Ca2+, twocomponents were required (figure 6.15A,C,E), whereas three compo-nents were needed to a comparable quality reconstruction in the pres-ence of Ca2+ (figure 6.15B,D,F). In both cases the agreement betweenexperimental data for the time-point ∆t = 20 ms, and the recon-structed data from linear decomposition, was less than satisfactory,indicating that the final conformational phase of the ChR2 photocycleis not necessarily following a single-exponential time-decay, which is

larger movement of the cytoplasmic portion of helices E and F whenin isolation, relative to SRII in complex with HtrII (figure 6.12, 6.13).This observation may explain why proton conduction in the cytoplas-mic portion of SRII is blocked by the presence of HrII [236]. Relateddisplacements have been observed of helix F in bR and SRII:HtrII us-ing spin-labeled EPR biophysical methods [237, 238], where the am-plitude of light-activated outward movement of helix F in both bRand SRII being described to be at minimum 1 Å [237], which is con-sistent with our modelling of TR-XSS data. Cryo-trapping of light-activated structural changes correlated with the M-state of SRII inisolation gave a mean Cα displacement from Pro144 to Pro175 of 0.75Å (PDB entry 3qdc [239]). Trapping studies of SRII:HtrII yieldedeven smaller Cα displacement of 0.34 Å for the same residues (PDBentry 2f95 [240]), and 0.47 Å (PDB entry [65]). Temperature TR-SSX study of SRII in isolation showed a mean Cα displacement of thesame residues to be 0.92 Å, which is larger than cryo-trapping studies,but nevertheless smaller amplitudes recovered by our analysis. Thissuggests that thaw/freeze protocols used during illumination of crys-tals of SRII:HtrII does not successfully capture the full extent of theproteins motion. This has also been observed in many cryo-trappingstudies of bR as reviewed by Wickstrand et al. [27].

With the recent improvements in structural modelling against TR-XSS data of integral membrane proteins [234], it seems that a largersampling of MD trajectories corresponding to SRIIS2 and SRII:HtrIIS2best fits may provide more detailed structural insights into signalpropagation between SRII and the HtrII transducer protein. Since,TR-XSS does not depend on crystal formation, one may reproducethese studies on a more full length transducer protein [241]. The ex-perimental and analytical application that we developed here can beextended to other biophysical studies [237,241] as a strong structuralcomplement to study the mechanism of signal propagation in haloar-chaea.

6.3 Time-resolved X-ray Solution ScatteringStudies of Structural Changes in Chan-nelrhodopsin 2 (PAPER III)

Channelrhodopsins (ChRs) are light gated ion channels that havebecome important model proteins in neuroscience and optogenetics[83, 242] , whereby light is used to depolarize neuronal membranes.Today ChRs and related proteins are used to study behaviours andlearning in animals from flies [243,244], to non-human primates [245].Channelrhodopsin-2 (ChR2) from the green alga Chlamydomonas rein-hardtii, are in nature involved in regulation of phototaxis, allowingunicellular alga to swim towards optimal light conditions [246].

Although the resting state conformation of ChR2 has been solvedto high resolution [89], questions remain regarding to what degreethe magnitude of conformational changes play, for the mechanism ofchannel opening and closing. Here we applied TR-XSS to probe thelight-induced structural changes in ChR2, both in the presence andabsence of Ca2+, it’s primary transported substrate.

Linear decomposition of the difference X-ray scatteringdata. Linear decomposition of the TR-XSS data were performed aspreviously described [178]. The procedure, uses a kinetic model fora sequence of two or three intermediate states, whereby the rate-constants are optimized for the best agreement between experimentaldata (black, figure 6.15A,B) and the sum of the basis spectra ex-tracted from linear-decomposition given the amplitudes predicted bythe kinetic model (red, figure 6.15A,B). In order to get a reasonablereconstruction of the TR-XSS data of ChR2-N24Q without Ca2+, twocomponents were required (figure 6.15A,C,E), whereas three compo-nents were needed to a comparable quality reconstruction in the pres-ence of Ca2+ (figure 6.15B,D,F). In both cases the agreement betweenexperimental data for the time-point ∆t = 20 ms, and the recon-structed data from linear decomposition, was less than satisfactory,indicating that the final conformational phase of the ChR2 photocycleis not necessarily following a single-exponential time-decay, which is

Figure 6.14: Overview of the function and photocycle of ChR2A) Cartoon representation of the different functionalities of microbialrhodopsins. Channelrhodopsin (mustard) is a light-gated ion channel; bac-teriorhodopsin (purple) is an outwardly directed light-driven proton pump;halorhodopsin (blue) is an inwardly directed light-driven chloride pump;sensory rhodopsin (orange) is a photoreceptor that relays a signal througha transmembrane transducer protein (blue). B) Overview of ChR2 pho-tocycle. The names, absorption peaks, and approximate time-scales of thedifferent spectral intermediates are indicated. Furthermore, approximatetime-scales of the open and closed states are also indicated.

consistent with spectral studies of ChR2, showing a long decay backto the resting structure [247]. Also, as with other studies of retinal-proteins [178,185], the first component (State 1) has a very low ampli-tude (blue line, 6.15E,F), which is not that much above signal-to-noisein this data after the removal of heat. As such, State 1 of both de-composition’s were not examined further. The second component ofChR2-N24Q (State 2) in the absence of Ca2+ shows more features incommon with the third component of ChR2 + Ca2+, with a Pearsoncorrelation of 84%. However, upon addition of Ca2+ ions, the ampli-tude of this difference signal is three times larger than that of ChR2without Ca2+. This observation is reinforced by the fact that IR-lasermeasurements from both samples were nearly identical, suggestingthat the level of photoactivation cannot be affected by the presenceor absence of the cation. On the other hand, ChR2-N24Q + Ca2+

state 2, is only weakly correlated to state 3 (Pearson correlation = 47%), and larger-amplitude oscillations can be observed in the domain0.05 Å−1 ≤ 0.25 Å−1 (figure 6.15F), for State 2 than State 3. Thesedifferences are a telling indication that the number, nature and ampli-tude of protein conformations are indeed all affected by the presenceof Ca2+.

Structural modelling Here we made a preliminary analysis ofpossible candidate motions by considering an outwards motion of he-lices E and F, that have been observed for bR [43, 248], and imposethem on upon the structure of ChR2 [89]. Figure 6.16A, demon-strates the results of this preliminary analysis using the tools de-scribed in [234]. Despite the oversimplified nature of the structuralmodel, several major features of State 2 of ChR2-N24Q are capturedin this analysis. In more detail, the dip in ∆S(q) in the domain 0.1Å−1 ≤ q ≤ 0.25 Å−1 is captured by the outwards motion of the cyto-plasmic portions of TM5 and TM6. This is also the case for the pos-itive features centered near q = 0.35 Å−1 and 0.6 Å−1. Even thoughthis analysis at this stage is not a comprehensive fit, the findings areconsistent with the notion of ChR2 undergoing light-induced struc-tural changes, as is the case with other microbial rhodopsins. Thisbest-fit derived from driving the structure towards a conformation in

Figure 6.14: Overview of the function and photocycle of ChR2A) Cartoon representation of the different functionalities of microbialrhodopsins. Channelrhodopsin (mustard) is a light-gated ion channel; bac-teriorhodopsin (purple) is an outwardly directed light-driven proton pump;halorhodopsin (blue) is an inwardly directed light-driven chloride pump;sensory rhodopsin (orange) is a photoreceptor that relays a signal througha transmembrane transducer protein (blue). B) Overview of ChR2 pho-tocycle. The names, absorption peaks, and approximate time-scales of thedifferent spectral intermediates are indicated. Furthermore, approximatetime-scales of the open and closed states are also indicated.

consistent with spectral studies of ChR2, showing a long decay backto the resting structure [247]. Also, as with other studies of retinal-proteins [178,185], the first component (State 1) has a very low ampli-tude (blue line, 6.15E,F), which is not that much above signal-to-noisein this data after the removal of heat. As such, State 1 of both de-composition’s were not examined further. The second component ofChR2-N24Q (State 2) in the absence of Ca2+ shows more features incommon with the third component of ChR2 + Ca2+, with a Pearsoncorrelation of 84%. However, upon addition of Ca2+ ions, the ampli-tude of this difference signal is three times larger than that of ChR2without Ca2+. This observation is reinforced by the fact that IR-lasermeasurements from both samples were nearly identical, suggestingthat the level of photoactivation cannot be affected by the presenceor absence of the cation. On the other hand, ChR2-N24Q + Ca2+

state 2, is only weakly correlated to state 3 (Pearson correlation = 47%), and larger-amplitude oscillations can be observed in the domain0.05 Å−1 ≤ 0.25 Å−1 (figure 6.15F), for State 2 than State 3. Thesedifferences are a telling indication that the number, nature and ampli-tude of protein conformations are indeed all affected by the presenceof Ca2+.

Structural modelling Here we made a preliminary analysis ofpossible candidate motions by considering an outwards motion of he-lices E and F, that have been observed for bR [43, 248], and imposethem on upon the structure of ChR2 [89]. Figure 6.16A, demon-strates the results of this preliminary analysis using the tools de-scribed in [234]. Despite the oversimplified nature of the structuralmodel, several major features of State 2 of ChR2-N24Q are capturedin this analysis. In more detail, the dip in ∆S(q) in the domain 0.1Å−1 ≤ q ≤ 0.25 Å−1 is captured by the outwards motion of the cyto-plasmic portions of TM5 and TM6. This is also the case for the pos-itive features centered near q = 0.35 Å−1 and 0.6 Å−1. Even thoughthis analysis at this stage is not a comprehensive fit, the findings areconsistent with the notion of ChR2 undergoing light-induced struc-tural changes, as is the case with other microbial rhodopsins. Thisbest-fit derived from driving the structure towards a conformation in

Figure 6.15: Time-resolved difference X-ray scattering data ofChR2-N24Q in the presence and absence of Ca2+, following photo-activationA) Time-dependant changes in the difference X-ray scattering,corresponding with ChR2-N24Q. B Time-dependant changes in the differ-ence X-ray scattering corresponding to ChR2 in 100 mM Ca2+. Black lineshows the experimental data and red line shows the data reconstructed fromthe basis spectra in panel E & F respectively. C) Time-evolution of spectralchanges correlated with the growth and decay of distinct states in ChR2-N24Q. D) Time-evolution of spectral changes correlated with the growthand decay of distinct states in ChR2-N24Q in presence of 100 mM of Ca2+.E) Basis spectra showing state 1 and 2 (top and bottom respectively), ex-tracted by linear decomposition of the ChR2-N24Q TR-XSS data. F) Basisspectra from linear decomposition of ChR2-N24Q + Ca2+ from TR-XSSdata. The first, second and third mean principal component is shown inblue, red and mustard, respectively. Time delays were ∆t = 2.5 µs, 6.3 µs,20 µs, 63 µs, 200 µs, 632 µs, 2 ms, 20 ms, which are evenly spaced on alogarithmic scale.

which the amplitude of the motions were very similar to that foundfor bR [43]. Conversely, structural fitting to State 3 of ChR2-N24Q re-covered a negative coefficient (figure 6.16B), which can be interpretedto indicate that the direction of motion in the model is opposite tothat which the difference X-ray scattering data suggest, meaning thatChR2 may be expected to compress somewhat relative to the restingstate conformation later in the photocycle.

Summary Convincing structural conclusions drawn from the linear-decomposition of the TR-XSS data after heat removal, demonstratesthat more conformational changes occur, and the amplitude of mo-tions larger in the presence of calcium ion as a substrate. This suggeststhat transient binding sites occur in ChR2 that involve rearrange-ments of α-helices, and an expansion of the radius of gyration of theprotein on the time-scale of a few tens of microseconds to milliseconds,which correlate approximately with the time at which ChR2 is openfor cation-transport (figure 6.15B). This strongly suggests that thesemotions are the ones governing ion permeability. On the contrary, thelater conformation of ChR2-N24Q had a best-fit with a conformationthat has a smaller radius of gyration than the resting conformation(figure 6.16B), implying that the protein undergoes a partial collapseas the cation is released. Even though the analysis here is preliminary,the overall quality of TR-XSS data together with the analysis toolsdeveloped [234], the comparative study of conformational dynamicsof ChR2-N24Q in presence or absence of Ca2+, offers a capability forelucidating the underlying mechanism of ion transport, by this im-portant model protein in optogenetics, which does not seem to beaccessible with other biophysical techniques. A more detailed analysisand structural fitting which samples a larger set of conformations viamolecular dynamics simulations together with modelling of potentialcation-binding sites, is necessary for a more comprehensive structuralmodel.

Figure 6.15: Time-resolved difference X-ray scattering data ofChR2-N24Q in the presence and absence of Ca2+, following photo-activationA) Time-dependant changes in the difference X-ray scattering,corresponding with ChR2-N24Q. B Time-dependant changes in the differ-ence X-ray scattering corresponding to ChR2 in 100 mM Ca2+. Black lineshows the experimental data and red line shows the data reconstructed fromthe basis spectra in panel E & F respectively. C) Time-evolution of spectralchanges correlated with the growth and decay of distinct states in ChR2-N24Q. D) Time-evolution of spectral changes correlated with the growthand decay of distinct states in ChR2-N24Q in presence of 100 mM of Ca2+.E) Basis spectra showing state 1 and 2 (top and bottom respectively), ex-tracted by linear decomposition of the ChR2-N24Q TR-XSS data. F) Basisspectra from linear decomposition of ChR2-N24Q + Ca2+ from TR-XSSdata. The first, second and third mean principal component is shown inblue, red and mustard, respectively. Time delays were ∆t = 2.5 µs, 6.3 µs,20 µs, 63 µs, 200 µs, 632 µs, 2 ms, 20 ms, which are evenly spaced on alogarithmic scale.

which the amplitude of the motions were very similar to that foundfor bR [43]. Conversely, structural fitting to State 3 of ChR2-N24Q re-covered a negative coefficient (figure 6.16B), which can be interpretedto indicate that the direction of motion in the model is opposite tothat which the difference X-ray scattering data suggest, meaning thatChR2 may be expected to compress somewhat relative to the restingstate conformation later in the photocycle.

Summary Convincing structural conclusions drawn from the linear-decomposition of the TR-XSS data after heat removal, demonstratesthat more conformational changes occur, and the amplitude of mo-tions larger in the presence of calcium ion as a substrate. This suggeststhat transient binding sites occur in ChR2 that involve rearrange-ments of α-helices, and an expansion of the radius of gyration of theprotein on the time-scale of a few tens of microseconds to milliseconds,which correlate approximately with the time at which ChR2 is openfor cation-transport (figure 6.15B). This strongly suggests that thesemotions are the ones governing ion permeability. On the contrary, thelater conformation of ChR2-N24Q had a best-fit with a conformationthat has a smaller radius of gyration than the resting conformation(figure 6.16B), implying that the protein undergoes a partial collapseas the cation is released. Even though the analysis here is preliminary,the overall quality of TR-XSS data together with the analysis toolsdeveloped [234], the comparative study of conformational dynamicsof ChR2-N24Q in presence or absence of Ca2+, offers a capability forelucidating the underlying mechanism of ion transport, by this im-portant model protein in optogenetics, which does not seem to beaccessible with other biophysical techniques. A more detailed analysisand structural fitting which samples a larger set of conformations viamolecular dynamics simulations together with modelling of potentialcation-binding sites, is necessary for a more comprehensive structuralmodel.

Figure 6.16: Results from preliminary structural modelling oflight-driven structural changes in ChR2-N24Q in 100 mM Ca2+.A) State 2 basis spectrum extracted from TR-XSS data (black) recordedfrom ChR2-N24Q in the presence of Ca2+ and the calculated basis spec-trum from an initial structural model (red) superimposed. B) State 3 basisspectrum extracted from TR-XSS data (black), recorded from ChR2-N24Qin the presence of Ca2+ and the calculated basis spectrum from an initialstructural model (red) superimposed.

Chapter 7

Concluding remarks andfuture outlook

Time-resolved X-ray solution scattering studies of integral membraneproteins has become a mature experimental method over the lastdecade. It is a highly reproducible method in terms of producing con-sistent difference XSS curves from light-activated integral membraneprotein, despite variations associated with experimental conditionsand sample preparation. Extracting the rise and decays of structuralintermediates from detergent solubilized membrane proteins and it’ssecondary structural changes from a sequence of time-dependant dif-ference XSS measurements has presented previously. However, theinterference between the protein and micelle had not earlier been ad-dressed. The analysis in this thesis provided first in PAPER I, aframework which allows an atomistic description of the protein anddetergent micelle using molecular dynamics simulations. Candidatemotions were extracted from serial crystallography data, and used toguide MD simulations. We identified from this how structural fluc-tuations within the detergent micelle influenced the difference X-rayscattering data, and how the micelle size influenced the the differ-ence X-ray scattering data. Interchanging the atomic coordinates ofthe protein micelle complex in resting and light-activated structuresalong a MD trajectory, allowed us to establish a protocol in whichthe protein-micelle X-ray scattering cross-term could be determinedwithout being dominated by random fluctuations within the deter-gent micelle. Solvent corrections for the excluded solvent was made,

Figure 6.16: Results from preliminary structural modelling oflight-driven structural changes in ChR2-N24Q in 100 mM Ca2+.A) State 2 basis spectrum extracted from TR-XSS data (black) recordedfrom ChR2-N24Q in the presence of Ca2+ and the calculated basis spec-trum from an initial structural model (red) superimposed. B) State 3 basisspectrum extracted from TR-XSS data (black), recorded from ChR2-N24Qin the presence of Ca2+ and the calculated basis spectrum from an initialstructural model (red) superimposed.

Chapter 7

Concluding remarks andfuture outlook

Time-resolved X-ray solution scattering studies of integral membraneproteins has become a mature experimental method over the lastdecade. It is a highly reproducible method in terms of producing con-sistent difference XSS curves from light-activated integral membraneprotein, despite variations associated with experimental conditionsand sample preparation. Extracting the rise and decays of structuralintermediates from detergent solubilized membrane proteins and it’ssecondary structural changes from a sequence of time-dependant dif-ference XSS measurements has presented previously. However, theinterference between the protein and micelle had not earlier been ad-dressed. The analysis in this thesis provided first in PAPER I, aframework which allows an atomistic description of the protein anddetergent micelle using molecular dynamics simulations. Candidatemotions were extracted from serial crystallography data, and used toguide MD simulations. We identified from this how structural fluc-tuations within the detergent micelle influenced the difference X-rayscattering data, and how the micelle size influenced the the differ-ence X-ray scattering data. Interchanging the atomic coordinates ofthe protein micelle complex in resting and light-activated structuresalong a MD trajectory, allowed us to establish a protocol in whichthe protein-micelle X-ray scattering cross-term could be determinedwithout being dominated by random fluctuations within the deter-gent micelle. Solvent corrections for the excluded solvent was made,

Chapter 7. Concluding remarks and future outlook

and showed that it does not have an effect on the overall fitting ofour data, but provides a low-angle correction for below q = 0.15 Å−1.The X-ray spectrum of the pink-beam undulator was specifically in-corporated into the analysis as well. The analysis tools and protocolsdeveloped here, indeed improved the structural modelling against ba-sis spectra extracted from TR-XSS data, and the modelling domainincreased in reciprocal space from 0.10 Å−1 ≤ q ≤ 1.18 Å−1, which inour previous research was 0.19 Å −1 ≤ q ≤ 0.7 −1 [178]. Furthermore,the formalism established allowed us to extend the new structuralmodelling protocol to investigate light-induced structural changes inSensory II in isolation and in complex with it’s transducer proteinHtrII (PAPER II), and Channelrhodpsin 2 (PAPER III).

We have also collected data on the eukaryotic visual rhodpsin intwo different detergent micelles (CHAPS,DDM) to investigate the in-fluence of different detergent types on the difference X-ray scatteringdata (see 7.1. This data has been collected at both linear accelerators(LCLS) and from synchrotron radiation sources (ESRF), and will beanalysed in the near future.

With the development of microfluidic mixing technologies for re-action initiation, it should be possible for the field of TR-XSS to movebeyond light-activated proteins. Time-resolved X-ray solution scatter-ing have already successfully used caged compounds to study proteindynamics [232]. Therefore, the analysis tools developed in this thesisshould provide a framework that anticipates the future evolution ofthe field.

Figure 7.1: Prelimary TR-XSS data of visual rhodopsin solubi-lized in two different detergent micelle models A) Difference X-ray scattering data of Visual rhodopsin solubilized in CHAPS, recordedat the linear accelerator LCLS. B) Difference X-ray scattering data of Vi-sual rhodopsin solubilized in CHAPS, recorded at ESRF-id09 beamline. C)Difference X-ray scattering data of Visual rhodopsin solubilized in DDM,recorded at ESRF-id09 beamline

and showed that it does not have an effect on the overall fitting ofour data, but provides a low-angle correction for below q = 0.15 Å−1.The X-ray spectrum of the pink-beam undulator was specifically in-corporated into the analysis as well. The analysis tools and protocolsdeveloped here, indeed improved the structural modelling against ba-sis spectra extracted from TR-XSS data, and the modelling domainincreased in reciprocal space from 0.10 Å−1 ≤ q ≤ 1.18 Å−1, which inour previous research was 0.19 Å −1 ≤ q ≤ 0.7 −1 [178]. Furthermore,the formalism established allowed us to extend the new structuralmodelling protocol to investigate light-induced structural changes inSensory II in isolation and in complex with it’s transducer proteinHtrII (PAPER II), and Channelrhodpsin 2 (PAPER III).

We have also collected data on the eukaryotic visual rhodpsin intwo different detergent micelles (CHAPS,DDM) to investigate the in-fluence of different detergent types on the difference X-ray scatteringdata (see 7.1. This data has been collected at both linear accelerators(LCLS) and from synchrotron radiation sources (ESRF), and will beanalysed in the near future.

With the development of microfluidic mixing technologies for re-action initiation, it should be possible for the field of TR-XSS to movebeyond light-activated proteins. Time-resolved X-ray solution scatter-ing have already successfully used caged compounds to study proteindynamics [232]. Therefore, the analysis tools developed in this thesisshould provide a framework that anticipates the future evolution ofthe field.

Figure 7.1: Prelimary TR-XSS data of visual rhodopsin solubi-lized in two different detergent micelle models A) Difference X-ray scattering data of Visual rhodopsin solubilized in CHAPS, recordedat the linear accelerator LCLS. B) Difference X-ray scattering data of Vi-sual rhodopsin solubilized in CHAPS, recorded at ESRF-id09 beamline. C)Difference X-ray scattering data of Visual rhodopsin solubilized in DDM,recorded at ESRF-id09 beamline

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