COMPLEMENTARY TECHNIQUES ENHANCE THE QUALITY AND SCOPE OFINFORMATION OBTAINED FROM SAXS.

David Lambright, Andrew W Malaby, Sagar V. Kathuria, R. Paul Nobrega, Osman Bilsel,C. Robert Matthews

Program in Molecular Medicine and Department of Biochemistry and Molecular Pharmacology,University of Massachusetts Medical School, Worcester, MA 01605, USA.

Uma Muthurajan, Karolin Luger,Howard Hughes Medical Institute, Colorado State University, Fort Collins, CO 80523, USA

Rajiv Chopra,Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA.

Thomas C. Irving, Srinivas Chakravarthy.The Biophysics Collaborative Access Team (BioCAT), Department of Biological Chemical and

Physical Sciences, Illinois Institute of Technology, Chicago, IL 60616, USA.

ABSTRACT

The increased technical versatility of synchrotron beam lines is enabling investigation ofincreasingly complex biological systems using a variety of biochemical and biophysical tools incombination with Small Angle X-ray Scattering (SAXS). The high flux delivered by thirdgeneration synchrotron sources allow incorporation into SAXS instruments a variety ofcomplementary biochemical or biophysical instrumentation that either directly improves dataquality or enhances the depth and resolution of the information obtained. We present here twosuch instances, 1. Size exclusion chromatography in line with the x-ray beam which facilitatespurification of the sample immediately before exposure; 2. Continuous-flow mixer incombination with a micro-focused beam that gives us access to kinetic information in the sub-millisecond time regime.

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1. INTRODUCTION

SAXS of biological molecules has been a technique of limited and specialized utility for manyyears but is now being used much more frequently for a large variety of samples andapplications. This is partly due to the wider availability of SAXS instruments on third generationsynchrotron sources. The excellent beam collimation and high flux densities from suchinstruments yield much better signal to noise at lower sample concentrations than required forconventional sources and instruments based on older generation synchrotrons. Developments onmany such SAXS facilities have focused on maximizing throughput through faster detectors androbotics [1, 2], but the same beam properties that permit high throughput also permit combiningSAXS with complementary biochemical and biophysical techniques that can greatly improve thesample monodispersity and therefore the quality and interpretability of the data and timeresolved measurements. Here we describe two such techniques that have been implemented atthe BioCAT Beamline 18 ID at the Advanced Photon Source (APS) - Argonne NationalLaboratory (ANL), namely combined Size Exclusion Chromatography with SAXS (SEC-SAXS)and in-line Continuous-Flow turbulent mixing (CF-SAXS). The current state of developmentsand future prospects are discussed.

2. SIZE EXCLUSION CHROMATOGRAPHY COMBINED WITH SAXS (SEC-SAXS)

2.1. Why SEC-SAXS?

Small angle X-ray scattering yields a variety of low-resolution information ranging from size,shape, and even internal structure of biomolecules. The quality and reliability of this informationis largely dependent on the homogeneity and monodispersity of the sample, which is normallyensured by employing elaborate purification protocols that come from thorough biochemicalcharacterization of the proteins, nucleic acids or protein – nucleic acid complexes of interest.However, despite our best efforts to bring the most homogeneous samples possible to the beamline, most samples encounter problems due to redistribution of small sub-populations into eitherlarger soluble oligomer and aggregate forms, or into smaller sub-components due to degradationof bigger complexes, which in turn adversely affect the scattering data quality or make itsinterpretation difficult. It would therefore be optimal in most cases to purify the bio-molecule ofinterest from the contaminants using a size-exclusion chromatography (SEC) columnimmediately before irradiation with X-rays. The ability of SEC-SAXS to enable acquisition ofreliable scattering data from otherwise non-homogeneous proteins that are susceptible toaggregation was first demonstrated at the BioCAT Beamline 18 ID in 2004 [3].

The equation I(q) α (N/V)V2particle (ρ1 - ρ2)

2FF(q)S(q) shows that the scattering intensity in aSAXS experiment is directly proportional to the square of the term (V2

particle) that signifies thesize of the particle [4]. This means that the aggregate fraction of a protein sample, even ifmanifested as a relatively small peak that is eluted at the void volume of an SEC column, canpotentially make large contributions to the scattering signal. This can be further compounded bythe presence of nucleic acids which have a higher electron density. Nucleosome core particles forexample, which consist of a histone protein core wrapped ~ 2 times by ~150 – 200 base pairs ofDNA are particularly susceptible to aggregation upon storage even for relatively short durationsbecause of the non-physiological (low ionic strength) buffer conditions used to store them afterreconstitution in vitro [5]. A miniscule amount of aggregate in a sample that contains largenucleo-protein complexes such as nucleosome core particles ~ 50% of which is DNA, can

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therefore make disproportionately large contributions to the scattering signal (Fig. 1A & B). Thepure nucleosome fraction therefore needs to be, and can be, separated from the aggregate fraction(however small) immediately prior to exposure to X-rays (Fig. 1A & B). Some proteins areprone to aggregation upon long term storage, even at -80˚C. Even if a small fraction of thesample aggregates it can significantly affect the quality of SAXS data (Fig.1C & D).

Figure 1. SEC-SAXS - Impact on Data Quality. (A) UV chromatogram from SEC column (Superose-6 10/300,GE Healthsciences) in-line with the beam shows that the aggregate (peak height at void volume, which is ~ 8ml forthis column) is a small fraction of the sample as shown by the relative height of the peak representing the purenucleosome fraction. (B) The SAXS sample flow cell was in simultaneously exposed for 1 second every 5 seconds.The scattering intensity is disproportionately higher for the aggregate compared to the UV peak, demonstrating theneed for SEC immediately before SAXS data collection. Each point represents the integrated intensity of individualframes and they are plotted in the sequence they were acquired. (C) Guinier approximation of protein X stored at -80°C and not purified by SEC immediately before exposure to X-rays. The non-linear relation between lnI and q2

indicates aggregation and furthermore it fails to satisfy the qmax*Rg < 1.3 condition characteristic of globularproteins. (D) Guinier approximation with SAXS data collected on the same sample of protein X as in C but using aSEC-SAXS setup. The protein was loaded onto a Superdex-200 10/300 and the eluted sample was exposed to thebeam for 1 second with a periodicity of 5 seconds. Selected individual exposures corresponding to the peak of theresulting chromatogram, after being reduced to the I(q) vs q one-dimensional plots were averaged before Guinieranalysis. The linearity of the relationship between ln I and q2 along with the fact that qmax*Rg < 1.3 indicates that thesample is aggregate free and SEC-SAXS is the right approach for samples of this nature. Guinier analysis in both C& D was performed using Primus [6] from the ATSAS suite of programs for SAXS data analysis.

There are other instances where the protein does not aggregate but tends to form oligomers in aconcentration dependent manner that can only be separated by SEC (Fig. 2A). Furthermore in arecent study, the combined use of size-exclusion chromatography, refractometry, and SAXS wasshown to be effective and essential in determining the structural model of a detergent coatedmembrane protein (aquaporin-0 surrounded by a n-dodecyl β-d-maltopyranoside corona) [7].

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It is therefore reasonable to posit that in most cases it is desirable to use SEC-SAXS to mostefficiently ensure sample monodispersity and the highest possible data quality.

2.2. Instrumentation:

The instrumentation required for SEC-SAXS, as shown in the first demonstration of thetechnique [3], consists primarily of an HPLC or an FPLC unit (AKTA-FPLC, GEHealthsciences, for all the experiments shown here). Several options are commercially availablesuch as the AKTA series (GE Healthsciences) or the NGC systems (Biorad). The column outletleads to a UV flow-cell, which in turn is usually connected to a fraction collector but in an SEC-SAXS setup it can be re-routed to the SAXS sample flow-cell instead. The most recent versionof the apparatus used at BioCAT consists of a 1.5mm quartz capillary (with 10µm walls) thatresides in a brass enclosure, which in turn is connected to a water bath for temperatureregulation. A variety of gel-filtration columns with a wide range of exclusion limits are availablefor purifying biomolecules of different sizes from providers like GE (superdex and superposecolumns) and Shodex (KW-400 and KW-800 series) (Table 1). Column size for analytical SECvaries from 3ml to 24ml, minimizing dilution of the sample and therefore ensuring high signal tonoise ratio. Flow rates may vary from 0.15ml/min to 1.0ml/min therefore making collection of afull data set from a pre-equilibrated column possible in a period of ~ 5 to ~ 50 minutes.

2.3. Data Processing Developments:

Raw data from a canonical SEC-SAXS experiment is a time series of one dimensional SAXSI(q) curves and a simultaneously recorded chromatogram from a UV or RI detector. While SEC-SAXS affords us the convenience of collecting a continuous data set with essentially a built-inconcentration series, which can be extrapolated to zero, the most obvious challenges are therelatively high volume of data and the subjectivity involved in determining the appropriateprocedure for buffer subtraction. Programs are being developed, which will enable automatedand objective SEC-SAXS data processing.

One example of such programs is a suite of novel software tools specifically conceived for SEC-SAXS within Ultra Scan – SOlution MOdeler (US-SOMO; http://somo.uthscsa.edu/) [8], whichis an open source program that primarily calculates conformational and hydrodynamicparameters for macromolecules based on their three-dimensional atomic structures. The SEC-SAXS module, in principle, treats the time sequence of I(q) vs q curves as a connected series ofchromatograms I(t) vs t [9]. Decomposition techniques are applied to the chromatography dataand are then used in combination with physical knowledge of the SAXS experiments to separatepeaks thus recreating separated I(q) vs q curves. Samples often exhibit non-baseline-resolutionduring SEC and sometimes a drifting baseline, which makes features such as Gaussiandecomposition of non-resolved peaks and baseline correction essential. Semi-automatedprotocols for extrapolation of radii of gyration (Rg), the weight averaged zero scattering angleintensities (I0) and molecular mass (Mw) from Guinier plots are also available.

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TABLE 1

Commercially Available Size-Exclusion Columns Suitable for SEC-SAXS

Column NameExclusion Limit(Mr)

RecommendedFlow Rate(ml/min)

Theoretical Plates*ColumnVolume (ml)

Superdex-200Increase 10/300

~ 1.3 X 106 0.75 > 48,000 m-1 24

Superdex-200Increase 5/150

~ 1.3 X 106 0.45 > 42,000 m-1 3

Superdex-75 5/150 ~ 1 X 105 0.15 > 25,000 m-1 24

Superdex-75 10/300 ~ 1 X 105 0.50 > 25,000 m-1 3

Superose-6 10/300 ~ 4 X 107 0.50 > 30,000 m-1 24

Superose-6 5/150 ~ 4 X 107 0.15 > 30,000 m-1 3

Superose-12 10/300 ~ 2 X 106 0.50 > 40,000 m-1 24

KW 802.5 ~ 1.5 X 105 1.00 > 21,000 per column ~ 15

KW 803 ~ 7 X 105 1.00 > 21,000 per column ~ 15

KW 804 ~ 1 X 106 1.00 > 16,000 per column ~ 15

KW 402.5-4F ~ 1.5 X 105 0.33 > 35,000 per column ~ 5

KW 403-4F ~ 6 X 105 0.33 > 35,000 per column ~ 5

KW 404-4F ~ 1 X 106 0.33 > 25,000 per column ~ 5

KW 405-4F ~ 2 X 107 0.33 > 25,000 per column ~ 5

* The number of theoretical plates can be calculated from a chromatographic peak after elution. N = 5.55 tR2/w2

1/2 .tR = The time between sample injection and the peak reaching a detector at the end of the column is termed theretention time. w1/2 is the peak width at half-height.

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A second example is DELA (Data Evaluation and Likelihood Analysis), which is an object-oriented, document-based application developed for 64-bit Intel Mac platforms running OSX10.6 or later. The intention was to create a graphically-oriented multipurpose interface for dataprocessing and analysis. The current (prerelease beta) version supports a variety of datainput/output formats, generates publication quality graphics for 2D plots exportable in commonimage formats, and provides tools for data editing, masking, annotation, and organization.Processing and analysis capabilities include basic transformations, statistics, error estimation,function interpretation, local and global fitting with a generalized maximum likelihood function,singular value decomposition (SVD), general linear least squares, linear combination, maximumentropy, and numerical simulation/fitting of reaction schemes. Methods are also provided forprocessing and analysis of isothermal titration calorimetry (ITC), surface plasmon resonance(SPR), and enzyme kinetic data. Processing, analysis, and plotting capabilities are extensiblethrough an interface for interpretation of scripts with a C-like syntax. The examples belowillustrate potential applications for processing and analysis of integrated SAXS data.

A suite of scripts was developed to facilitate basic SAXS analyses (e.g. Guinier approximation)and especially to explore novel approaches for processing and analysis of SAXS data setscollected with in-line liquid chromatography. The latter approaches may be particularly useful incases where appropriate reference data sets for buffer subtraction are difficult to identify orunavailable. In SEC-SAXS, for example, the presence of multiple, overlapping oligomericspecies can complicate solvent subtraction, since data sets near the peak of interest may becontaminated by scattering from overlapping oligomeric species while remote data sets arecollected at substantially different times. To circumvent these issues, an alternative approachwas investigated in which solvent subtraction was replaced with SVD-based reconstruction ofthe macromolecular scattering contribution using a set of optimal linear coefficients derived froman automated analysis of the linearity of the Guinier region (Fig. 2). For the cases examined, theresults appeared to be comparable or better than direct reference subtraction. As presentlyimplemented, the approach is limited to cases where only two significant scattering contributions(representing the macromolecule and solvent) are present in the data sets used for SVD. A plotof the SAXS chromatogram in conjunction with a graphical tool and associated scripts facilitatedselection of SAXS data sets satisfying this criterion. In addition to the SAXS scripts, maximumentropy method (MEM) [10] pair distributions functions, P(r), can be calculated with flat or sinepriors.

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Figure 2. Examples of Processing and Analysis of SEC-SAXS Data using DELA. (A) Selection of SAXS datasets using a plot of the mean scattering intensity as a function of the index of the SAXS data sets collected duringelution of monomer and dimer peaks from an in-line gel filtration column. (B) Plot of the singular values Snn fromsingular value decomposition (SVD; SAXS data set matrix = U•S•VT) of the data sets selected in A. The columns ofU are orthonormal basis components spanning the space of the original data matrix, S is a diagonal matrix ofweights known as the singular values, and the orthonormal columns of V contain the relative contributions of thebasis components (columns of U) to each of the data sets. Two significant components (U0 and U1) representing amixture of solvent and protein scattering are revealed by the magnitude of singular values. The remainingcomponents represent noise, as indicated by the small monotonically decreasing singular values and by lowautocorrelation of the corresponding columns of U and V. (C) Determination of the optimal coefficient c0 for thelinear combination c0•U0 + c0•U1 by automated analysis of the linearity of the Guinier region with c1 set to aconstant value of 1. (D) Plot of the optimal linear combination from C compared with the crysol fit for the crystalstructure of the same construct. (E) Guinier plot and fit of the optimal linear combination in D. (F) Comparison of

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maximum entropy method (MEM) and GNOM P(r) distributions. The MEM distribution was calculated with a flatprior.

We are moving rapidly towards developing more efficient ways to collect, process, and interpretSEC-SAXS data, so that it may become the standard operating procedure for most if not all bio-molecular SAXS experiments at the BioCAT beamline 18ID.

3. SAXS WITH IN-LINE CONTINUOUS-FLOW MIXER (CF-SAXS)

3.1. Why CF-SAXS?

It is becoming increasingly clear that in order to study complex biological processes such asprotein and RNA folding, we need access to information at time points less than 0.1milliseconds, which is not obtainable by conventional methods. Using the in-line stopped-flowsetup (SFM-400, Bio-Logic) at BioCAT (APS), studies in the past have reported the multi-stagecollapse of bacterial ribozyme in the sub-millisecond time regime (Roh et al., 2010) but recentstudies in the protein folding field using other biophysical techniques such as tr-FRET (Time-Resolved Fluorescence Resonance Energy Transfer) have shown that there is a wealth of kineticsinformation in even earlier time points [11]. SAXS would be a powerful complementarytechnique if we were able to access structural information at the time-points sampled by tr-FRET. Continuous flow mixing is perhaps the most reliable and most widely applicabletechnique to initiate sub-millisecond mixing reactions. This technique has been successfullyapplied to several systems, including several proteins and RNA folding in conjunction withseveral different methods of detection. Among detection techniques that measure structuralchanges in unlabeled biomolecules at the microsecond time scales, SAXS provides a reliablemeasure of global changes in size, shape and structure. These measurements can be readilycompared with computed structural parameters and provide a direct means to validatecomputational simulations of molecular dynamics. The most recent attempts at continuous-flowtime-resolved SAXS at BioCAT beam line 18ID (APS) have given access to the time regimefrom 100µs to ~ 2.4ms, which readily confirmed our conclusions from the tr-FRET studies.

3.2. Instrumentation:

The basic design for the continuous flow mixer (Fig. 3a) has been reported previously by Bilselet al [12]. The design, dimensions, and assembly of this modified SAXS compatible continuousflow mixer and how it can be incorporated into a micro-SAXS instrument at the BioCAT beamline 18ID have been described elsewhere [13]. Briefly, under continuous flow conditions,distance along the observation channel corresponds to the reaction time. A mixing-time of ~ 100microseconds has been reported by using this mixer in conjunction with our Kirkpatrick-Baezmirror based micro-focus setup [14] that can achieve a beam profile of 5 microns (vertical focus)by 20 microns (horizontal focus) and a flux of ~1013 photons/s. At 8 KeV and a camera length of0.5 M we obtained a q-range of 0.02 to 0.4 Å-1. The 20 mm observation channel was scanned ata speed of 1 mm s-1 starting at 0.25 mm with a frame rate of 5 frames s-1 for 19 s (total 95frames) ending 0.75 mm from the exit port. At a flow speed of 20 ml min-1, which correspondsto a flow velocity of 120 µs mm-1, the mixing of the solutions is complete within the first mm ofthe channel. This distance corresponds to ~ 93 microseconds.

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Figure 3. Access to Sub-Millisecond Time Regimes using the Continuous-Flow Mixer. (A) The unfoldedprotein and refolding buffer are mixed in a 1:9 ratio inside the arrow shaped mixer, flowing at a final flow rate of 20ml min-1. The 30 micron wide mixing region expands out into a 100 micron wide, 20 mm long observation channel.At the highest flow rate, 20 ml min-1, the mixing is completed within 1 mm from contact of the mixing solutions,corresponding to a 93 µs dead-time. The first frame after the completion of mixing, frame 3, corresponds to a timeof ~ 100 +/- 12 µs. The time range accessible to this experiment ranges from 100 microsecond to a few ms. (B)Circularly reduced image of frame 3 (100 µs after initiation of refolding) with 1.5 mg ml-1 pWT-CheY underrefolding conditions (0.8 M urea and 10 mM potassium phosphate pH 7.0). The distribution of intensities at each Qis plotted as a probability and represented in 3D. The distribution at high Q is very smooth as there is very littleparasitic scatter occurring in this region, however the low Q region appears to have multiple small peaks in closeproximity to each other and some scattered points at very high I(Q). The scattered peaks at low Q with highintensities are easily masked out by setting automatic threshold cutoffs in the averaging routine.

The first frame after this region (Frame 3) is centered at 100 microseconds and time-averagesbetween 88 and 112 microseconds, with nearly even distribution. The 200 ms frame rate resultsin a time increment of 24 microseconds per frame. The accessible time regime under theseconditions was 100 microseconds to 2.3 ms. Longer times (~ 4.5 ms) can be accessed byslowing the flow rate down to 10 ml min-1. With the high beam flux and 190 ms exposure times,the sample concentration of E.coli CheY, a 14 kDa protein, in 10 mM Potassium phosphate

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buffer and 0.8 M Urea at pH 7.0, could be dropped to 1.5 mg ml-1 to produce high quality data(Figure 3b).

3.3. Data Processing Developments:

Data collection while scanning the channel continuously requires a very precise alignment andmovement of the mixer with respect to the beam. While this setup results in a high duty cyclefor the experiment (>80%), there is also in an increase in parasitic scatter from the Kaptonwindows of the mixer that is different at each frame of the mixer. Data reduction thus requires asignificant level of preprocessing of the image to counter this parasitic scatter. This can beachieved by either developing masks for each of the frames individually, which is inefficient andresults in a loss of usable pixels, or as reported by Graceffa et al [13] by applying an automatedthreshold masking algorithm in Fit2D. As the data collection requires a fast photon countingdetector, the Pilatus 100, a future direction for potential processing software development is totake advantage of the lack of electronic noise in the images to identify the parasitic scatter in aquantitative and automated manner. Figure 3b is a 3D view of a single image circularly reducedto show the distribution of pixel intensities at different Q. From this representation the limitationof the threshold masking method is immediately apparent in that only the noise at high intensitiescan be removed by this method. The theoretical Poisson distribution of the intensity at each Qcan be used to determine the true data distribution and obtain higher quality circular averageddata (Fig. 4).

Figure 4. Issues with CF-SAXS Data Analysis. Comparison of the distribution of intensities at two frames (Frame3 – 0.1 ms and Frame 80 1.9 ms). The intensity distribution at low Q for the two frames with either buffer or proteinis shown with a fit of a poisson distribution around the main peak. The outliers are distinctly different between thetwo frames, indicating the need for treating each frame to a separate mask.

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The method would involve reducing each image to obtain a distribution of intensities at each Qand perform a robust fit using a Poisson distribution to obtain the mean and variance. Analternative approach would be to resample the image as a function of the azimuthal angle andperform SVD to find the linear combination of significant basis vectors to reconstruct the closestleast-squares approximation to a circularly symmetric scattering profile. This general approachcan therefore potentially filter noise and parasitic scattering in a model independent way. Ifsufficient photon statistics are available each image could be treated independently without theneed for a user-generated mask.

4. FUTURE PROSPECTS

SEC-SAXS is amenable to a high degree of automation. New and improved FPLC -instrumentation allows loading of multiple samples consecutively without user intervention andalso allows for multiple columns to be connected simultaneously to a column valve that can beprogrammed to run different columns for different samples depending on their size and theexclusion limit of the columns. We also hope to upgrade the detector system to a large area PixelArray Detector with significantly shorter delay between frames, enables us to record 5-10 timeshigher number of exposures as the sample elutes from the column. Furthermore the new detectoralso promises much higher signal to noise ratios and broader q-ranges. This along withsophisticated software to process, and interpret large amounts of data generated by a SEC-SAXSsetup can make this the optimal approach to collect most if not all SAXS data.

The continuous flow setup described here requires hundreds of milligrams of protein perexperiment. We hope to significantly decrease the sample consumption by trying alternativeswith relatively lower dead volumes compared to the current pump arrangement (ISCO Teledyne500D), for instance, the Harvard Apparatus continuous flow syringe pumps, thus rendering aheretofore expensive technique more accessible to a wider range of biological samples. We areexploring the possibility of replacing the KB mirror system with a compound refractive lens(CRL), which can generate a focused beam that is 1µm vertically and 20µm horizontally. Thishighly focused beam will enable the use of smaller mixer channels, which in turn can potentiallyfacilitate sampling of earlier time points. While we have successfully processed CF-SAXS datausing the aforementioned automated threshold masking approach [13], we see an obvious needfor optimizing this aspect of the analysis and are currently working on implementing theapproaches mentioned above.

5. ACKNOWLEDGEMENTS

David Lambright, and Andrew Malaby’s work was supported by the grant GM 056324 (NIH).Sagar Kathuria, Paul Nobrega, and Osman Bilsel’s work was supported by grants GM23303(NIH) and MCB1121942 (NSF). Uma Muthurajan and Karolin Luger’s work was supported bythe grants GM067777 and GM088409 (NIH) and The Howard Hughes Medical Institute. Use ofthe Advanced Photon Source, an Office of Science User Facility operated for the U.S.Department of Energy (DOE) Office of Science by Argonne National Laboratory, was supportedby the U.S. DOE under Contract No. DE-AC02-06CH11357. This project was supported bygrants from the National Center for Research Resources (2P41RR008630-17) and the NationalInstitute of General Medical Sciences (9 P41 GM103622-17) from the National Institutes ofHealth. We thank Dr. Emre Brookes (UTHSC) for helpful discussions on SEC-SAXS data

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analysis tools and permission to discuss US-SOMO. The content is solely the responsibility ofthe authors and does not necessarily reflect the official views of the National Center for ResearchResources, National Institute of General Medical Sciences or the National Institutes of Health"

6. REFERENCES

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