X-Ray Diffraction Experiment

$: X-Ray Diffraction Experiment$
X-RAY DIFFRACTION EXPERIMENT—THE LAST EXPERIMENTIN THE STRUCTURE ELUCIDATION PROCESS

By MAKSYMILIAN CHRUSZCZ,*,‡,§ DOMINIKA BOREK,†,‡,§

MARCIN DOMAGALSKI,*,‡,§ ZBYSZEK OTWINOWSKI,†,‡,§ ANDWLADEK MINOR,*,‡,§

*Department of Molecular Physiology and Biological Physics, University of Virginia, 1340

Jefferson Park Avenue, Charlottesville, Virginia 22908†Department of Biochemistry, The University of Texas Southwestern Medical Center at Dallas,

5323 Harry Hines Blvd., Dallas, Texas 75390-8816‡Midwest Center for Structural Genomics

§Center for Structural Genomics of Infectious Diseases

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24II. Experimental Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25III. Choice of Diffraction Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27IV. Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30V. Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33VI. Data and structure quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34VII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

ABSTRACT

Recent years have brought not only an avalanche of new macro-molecular structures, but also significant advances in the proteinstructure determination methodology only now making its way intostructure-based drug discovery. In this chapter, we review recentmethodology developments in X-ray diffraction experiments thatled to fast and very accurate elucidation of three-dimensional struc-tures of macromolecules. We will discuss the role of data collection asthe last experiment performed in the crystal structure determinationprocess. A statistical analysis of diffraction experiments that arereported in the Protein Data Bank (PDB) is also presented.

ADVANCES IN PROTEIN CHEMISTRY ANDSTRUCTURAL BIOLOGY, Vol. 77DOI: 10.1016/S1876-1623(09)77002-0

23 Copyright 2009, Elsevier Inc.All rights reserved.

I. INTRODUCTION

There are several experimental techniques that can be used toelucidate the three-dimensional structures of protein and viruses:X-ray crystallography, electron microscopy (EM), small-angle X-ray scattering (SAXS), and nuclear magnetic resonance (NMR)spectroscopy are among them. The application of the two mostfrequently used techniques—NMR spectroscopy and X-ray crystal-lography—has resulted in the deposition of almost 60,000 struc-tural models of biological macromolecules (proteins, DNA, RNA,or viruses) into the Protein Data Bank (PDB) (Berman et al., 2002).The ratio of X-ray and NMR structures is about 6:1. Thus X-raycrystallography is still the predominant technique used to deter-mine the structures of biological macromolecules.High-throughput structural biology initiative—and in particular,

structural genomics (SG)—efforts have solved thousands of proteinstructures and amassed a vast amount of experience (Burley et al.,2008; Slabinski et al., 2007). This experience has resulted in manyadvances that have improved the success rate of all experimental stagesof the structure determination process, namely cloning, expression,crystallization, data collection, data reduction, phasing, model building,and structure deposition and analysis. In particular, the experience ofSG groups has resulted in many efficient new techniques and technol-ogies for crystallographic data collection and structure solution.In principle, X-ray diffraction experiments are simple, and have not

changed much from the time almost 100 years ago when Max vonLaue (with the assistance of Walter Friedrich and Paul Kipping)obtained photographs showing the X-ray diffraction pattern pro-duced from zinc sulfide. In modern-day macromolecular X-ray dif-fraction experiments, the two-dimensional diffraction images arecollected with the use of an experimental setup that usually has fourcomponents: an X-ray source, a goniostat that can drive the crystalalong the spindle axis, an area detector, and equipment for thecryocooling of macromolecular crystals. The difficulty of the experi-ment arises from the fact that the crystals used in macromolecularcrystallography vary enormously. For example, crystal mosaicity mayvary between 0.05˚ and 3.5˚, unit cell volumes may vary between

24 MAKSYMILIAN CHRUSZCZ ET AL.

10,000 and 720,000,000 A3, and every crystal may have additionalproblems like anisotropic mosaicity, twinning, and so on. Selectingthe best crystal for diffraction is critical as in many cutting-edgeexperiments the number of available crystals can be very limited. Inextreme cases, there may be only one crystal available. In addition, itis very difficult to identify experimental setup pathologies, as thecorrelations between crystal and equipment malfunctions are veryunintuitive. For example, goniostats that use servomotors to drivethe spindle axis produce poor quality data when almost perfect crys-tals with unusually low mosaicity are in the X-ray beam. For typicalmacromolecular crystals, with mosaicity of 1˚ or larger, the sameexperimental setup may yield ideal data, as the (effectively) largermeasurement time averages the nonuniformity of the spindle axismovement. This example shows that knowledge of experimentalsetup limitations can be critical for the success of the experiment.Experimenters who are aware of the limitations of nonuniform spin-dle axis rotation may address the problem in one of the two equivalentways: either use another synchrotron station with a spindle axis drivenby a stepper motor, or select a more mosaic (and unfortunately, lowerquality) crystal or slower rotation with attenuated beam.

II. EXPERIMENTAL PROTOCOLS

In current practice, macromolecular diffraction data are typically col-lected for three different purposes: phase determination by molecularreplacement (MR), phase determination by multi-wavelength anoma-lous diffraction (MAD)/single-wavelength anomalous diffraction (SAD),and data for the final refinement of a model. Other techniques are alsosometimes used, such as phasing by multiple isomorphous replacement(MIR) or ab initio methods, albeit much less frequently than the onesdescribed above. In each case, the diffraction experimental protocolshould be designed to produce data best suited for the purposeintended. In the first and third cases, the diffraction experiment is usedto measure amplitudes and phases are derived from the known model.In the second case (SAD/MAD), the diffraction experiment measures aphase-sensitive signal that is typically produced by small differences

DATA COLLECTION 25

between observed amplitudes (Hendrickson, 1991). Often datacollected for MR or SAD/MAD phasing are later used for therefinement of the structure. In that case, the experimental collec-tion protocol is a compromise between a protocol optimized forMR phasing (complete data to a lower resolution optimal forphase determination by MR) and a protocol yielding a completedata set collected to the highest resolution possible (the diffractionlimit).In principle, the data collection protocols should be simple as there

are typically only four parameters that are under the user’s control:crystal-to-detector distance, exposure time, oscillation angle, and wave-length of X-ray radiation (Bourenkov and Popov, 2006; Popov andBourenkov, 2003). The difficulty of the experiment arises from the factthat not only is the quality of the crystal beyond the experimenter’scontrol, but also destruction of the sample with the X-ray radiationcause crystal decay. The difficulty of choosing user-controlled para-meters (protocol) that minimize the detriments of poor crystal qualityand radiation decay and maximize the signal of interest is to decidewhat tradeoffs are most beneficial for a particular crystal, its applica-tion, and experimental setup. For example, using a longer exposuretime for each image produces better counting statistics but may lead toincomplete high resolution data due to crystal decay. In addition,longer exposure time may lead to incomplete low resolution databecause of the limited dynamic range of most detectors that are cur-rently used at synchrotron facilities (Fig. 1). For the latter case, it maybe appropriate to collect data in multiple passes, measuring the lowresolution data first (Dauter, 2005). A small oscillation range helps toavoid overlaps of diffraction spots and for other reason is in principlebeneficial to data quality (Pflugrath, 1999). However, smaller oscilla-tions may be detrimental with experimental systems with poor shutter/goniostat synchronization and/or detectors with relatively large read-out noise. Another parameter is detector distance; moving the detectorfarther away from the sample decreases X-ray noise and helps to avoidoverlaps for large unit cell crystals but limits the resolution of therecorded diffraction data. A list of possible tradeoffs is long. The bestillustration of all of these difficulties is one fact: even though there


are many programs that calculate the best collection strategy for dif-fraction experiments, it has been reported the need to collect 57 fulldata sets on average (Holton, 2005) to make one PDB deposit.

III. CHOICE OF DIFFRACTION PROTOCOL

Data collection is the last experiment that is performed during thecrystal structure determination process. As diffraction experimentsmeasure only intensities (which are related to amplitudes of structurefactors), the intended method for generating phases corresponding tothose intensities must be decided before the diffraction experimentbegins. This is necessary to optimize the data collection protocol togenerate data most suited for the intended goal. In the simplest case,data for structure refinement, it is enough to concentrate on collectionof an accurate and complete data set. The major goal of such anexperiment is to obtain the highest possible resolution of the data.

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tion

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FIG. 1. Fraction of deposits determined with the use of CCD detectors by datacollection year. The blue bars represent the fraction of PDB deposits solved usingsynchrotron radiation, while the red bars represent the fraction of deposits solvedusing home systems data for 2009 are incomplete. (See Color Insert.)

DATA COLLECTION 27

The data collection protocol for MR phasing (Rossmann, 1962,1964) is usually very similar to the protocol for experiment describedabove. However, in the case of MR, more care has to be taken tocollect the low resolution data, as high resolution data are not criticalto generate a MR solution. Specifically, complete low resolution dataare very important for all methods that use Patterson functions (suchas MR). That having been said, the success of MR depends more onthe availability of an appropriate search model (Lebedev et al., 2008)than on the accuracy of the data. It was shown recently that searchmodels do not need to have very high sequence identity to thestructures of interest (Fig. 2), and in some cases MR methods havebeen successful for search structures with less than 20% sequenceidentity to the experimental structure. Nevertheless, even having ahighly homologous structure does not guarantee the success of MR.Having many copies of the macromolecule in the asymmetric unit hasbeen a serious obstacle for MR, although recent developments ofsoftware and methodology have produced greatly improved

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25 35 45Sequence identity (%)

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FIG. 2. The resolution of structures solved by MR as a function of the sequenceidentity of the most similar homolog in the PDB at the time of deposition. The figureshows only cases where the sequence identity of the most similar structure smaller orequal to 90%. Red circles represent structures with one macromolecular chain in theasymmetric unit. (See Color Insert.)


multicopy translation and rotation searches (McCoy, 2007; Vagin andTeplyakov, 2000). In other situations, noncrystallographic symmetryoperators are close to the crystallographic symmetry operators. Thisresults in “pseudosymmetry,” which may additionally complicate oreven prevent phase solution by MR (Dauter et al., 2005; Jaskolskiet al., 2006). This problem is not rare; it has been estimated thataround 6% of all structures deposited in the PDB exhibit pseudosym-metry (Zwart et al., 2008).If there is no suitable model for MR or the MR method fails for any

reason, the only possibility is de novo phase determination. SAD/MADmethods (Dauter et al., 2002) are the most popular experimentalphase determination techniques (Grabowski et al., 2009), and in thelast 10 years have effectively eliminated the application of MIR/singleisomorphous replacement (SIR) methods. In particular, SAD hasgained significant popularity (Chruszcz et al., 2008), because of itsrelative experimental simplicity and development of improved SADphasing programs (Wang et al., 2006). As a result, SAD is becomingthe most often used technique to phase structures of “novel” macro-molecules (Chruszcz et al., 2008). Despite its “simplicity,” SAD techni-ques require the most accurate data and is the most demanding in thesense of diffraction protocols, hardware, and software. Sometimes, auseful anomalous signal can be obtained for proteins that have onlyone ordered selenium atom per 200 residues (Fig. 3). In principle,almost all proteins (with sufficient methionine and/or cysteine resi-dues) could be phased using the anomalous signal from sulfur (Dauteret al., 1999; Hendrickson and Teeter, 1981; Wang, 1985). Unfortu-nately, sulfur SAD is typically only successful for well-diffracting crys-tals and the highest quality hardware and software combinations. Inall other cases, the macromolecule sample must be derivatized beforedata collection, as only a fraction of macromolecules naturally containanomalously scattering elements other than sulfur in quantities suffi-cient for successful phasing. For example, metal-ion-containingproteins may not require any special treatment (Ascone and Strange,2009; Shi et al., 2005), but measuring the X-ray absorption spectrumof the protein crystal to confirm presence of particular element beforedata collection is recommended (Walsh et al., 1999).

DATA COLLECTION 29

IV. EXPERIMENTAL SETUP

Cryocooling is the only major addition to the current experi-mental procedure in comparison with the procedure used by Maxvon Laue 100 years ago (he also did not use rotational method).Low temperatures (~100K) are used in almost all recent macro-molecular diffraction experiments (Chruszcz et al., 2008). In 2009,90.5% of all PDB deposits reported data collection temperaturesbelow 130K. Cryocooling (Garman and McSweeney, 2007; Gar-man and Owen, 2006; Hope, 1988), in connection with the use of

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SE

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io (

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idue

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FIG. 3. Ratio of total amino acid residues to number of ordered Se-Metresidues, plotted as a function of the structure resolution. Structures solved bySAD are shown as red circles and those solved by MAD as green circles. (See ColorInsert.)


cryoloops (Teng, 1990), has enormously increased the success rate ofdata collection process, by decreasing—but not eliminating—the rateof radiation decay of protein samples. However, the choice of cryo-solution is still a challenge andmany diffraction patterns are obtainedfrom suboptimal cryocooling conditions. New software under devel-opment (Borek et al. personal communication; Otwinowski et al.,2003) can correct the values of intensities to account for radiationdecay, but they are not able to restore high resolution reflections thatcannot be measured due to crystal damage.The fact that macromolecular crystals are typically much smal-

ler and diffract orders of magnitude more weakly than thecrystals used for diffraction 100 years ago the small moleculecrystals prompted the development of high brilliance X-raysources.Currently, almost 80% of all PDB deposits report the use of

synchrotron radiation. This fraction is even higher when nonre-dundant structures (defined as the set of protein structures withless than 30% sequence identity to structures previously reportedto PDB) are taken into account (Grabowski et al., 2009). Synchro-trons not only provide high brilliance radiation source but mostimportantly allow to change energy (or wavelength) of the incom-ing radiation, which is critical for SAD/MAD experiments. Themajority of currently existing over 120 macromolecular crystallo-graphy synchrotron stations permit the use of a wide range ofenergies. However, an analysis of the wavelengths used for struc-ture determination of PDB deposits shows that only a small frac-tion was solved with collection wavelengths outside the 0.9–1.0 Arange (Fig. 4). It seems that it would be beneficial for the struc-tural biology community to build cheaper and simpler high-intensity fixed wavelength stations with energy set at the seleniumabsorption edge.The biggest technological change affecting macromolecular diffrac-

tion experiments is in the development of devices used to recorddiffraction patterns. Ordinary film was still in use at most synchrotronstations as of the mid-1980s but soon was replaced by the TV detectorconstructed by Ulli Arndt for Enraf-Nonius (Schierbeek and

DATA COLLECTION 31

Parlevliet, 1991), and Image Plate (IP) detectors developed by MAR,Rigaku, and Mac Science (Sakabe, 1991; Sakabe et al., 1997). At thesame time, Nicolet and San Diego Multiwire (currently Area DetectorSystem Corporations (ADSC)) independently developed very suc-cessful multiwire detectors for use with rotating anode X-ray

0.70

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8000# of

PD

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its

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0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Wavelength (Å)

FIG. 4. PDB deposits grouped by the wavelength used during the diffractionexperiment. Only deposits obtained with synchrotron radiation are shown.


generators (Hamlin et al., 1975, 1981). At the beginning of the 1990s,a new charge-coupled device (CCD) detector (Gruner, 1994; Gruneret al., 2002) constructed by Sol Gruner was extremely successful atCHESS synchrotron. Now, CCD detectors are responsible for almost100% of data collected at synchrotron stations and are frequentlyused on “home” X-ray diffraction stations as well (Fig. 1). In 2008, anew Pixel Array Detector became operational at the Swiss LightSource (Bech et al., 2008). The very fast readout time of that detectorallows for shutterless data collection and full characterization of acrystal within a few seconds.The goniostat used for macromolecular diffraction experiments

can be very simple—just a single rotation axis can be used to collecta complete data set. Few or no observations are lost in the so-calledblind region of missing reflections, given the relatively low resolu-tion limits typical for macromolecular experiments. A three-axisgoniostat seems to add some benefits compared to a single-axissystem but requires additional precautions because of the possibilityof physical collisions between the goniostat and the detector, cryo-cooling device, or other equipment. Experience has shown thatgoniostats are the most neglected components of the X-ray diffrac-tion apparatus in terms of technical advancement. Most experimen-tal errors in macromolecular X-ray diffraction are caused by unevengoniostat movement and/or poor coordination between the gonio-stat and the X-ray shutter.

V. DATA PROCESSING

Software development and software/hardware integration plays oneof the major roles in the transformation of structural biology into ananalytical tool that can be more easily used by noncrystallographers.Traditionally, the result of a synchrotron experiment was a set ofdiffraction images, or at best, scaled diffraction intensities with asso-ciated errors. During the last 3 years, the trend is toward integratedsystems that combine data collection, data reduction, phasing, andmodel building. Such systems significantly accelerate the process ofstructure determination and on average minimize the number of data

DATA COLLECTION 33

sets and amount of synchrotron time required for structure solution.The typical end result (while the crystal is still on the goniostat) is aninterpretable electron density map with a partially built structure and,in some cases, an almost complete refined model. The current devel-opment of systems like HKL-3000 (Minor et al., 2006) or PHENIX(Adams et al., 2002) is oriented toward very fast structure solution toprovide feedback during the diffraction experiment. HKL-3000 hasproved its usefulness as over 1000 PDB deposits list it as the programused for structure determination.

VI. DATA AND STRUCTURE QUALITY

In an ideal world, the only parameter that should describe structurequality is the resolution of the data used for structure refinement. Inpractice, however, there is no agreement how to define the resolutionof the data. The analysis of mean I/�I presented on Figure 5 showsthat if we disregard obvious mistakes in deposits (e.g., if the mean I/�Ifor the highest resolution shell is the same or larger than the mean for

00

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5 10 15I/σ l

I/σ l (

high

est r

esol

utio

n sh

ell)

20 25 30

FIG. 5. Distribution of mean I/�I versus I/�I for the highest resolution shell forstructures reported in the PDB.


the whole data set), a substantial fraction of the remaining structureshad mean I/�I values much larger than 5 in the highest resolutionshell, which suggests that refinement of many structures are notperformed to the true crystal diffraction limit. Moreover, severaldeposits have very incomplete data in the highest resolution shell(Grabowski et al., 2009). Similar conclusions may be derived by ana-lyzing Rmerge values (Fig. 6). The quality of the data does not directlyimplicate the quality of structures. It is striking, however, that thequality of structures as represented on an R versus resolution plot(Fig. 7) differs substantially for all four large PSI2 centers (JCSG,MCSG, NESGC, NYSGXC), even when only structures depositedduring the last 2 years are taken into consideration.

VII. CONCLUSIONS

Inexperienced experimenters can be assured that they are closeto performing the best experiments possible by preparing

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Rmerge

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FIG. 6. Distribution of Rmerge versus Rmerge for the highest resolution shell forstructures reported in the PDB.

DATA COLLECTION 35

experiments based on the analysis of successful experiments andprotocols used on the most productive synchrotron stations (Gra-bowski et al., 2009; Rosenbaum et al., 2006). Unfortunately, it ismuch easier to analyze single experiments than to find out in gen-eral what experimental practices are the most successful (in thesense of successful structure solution). The currently used formatof the PDB header (Berman et al., 2002) does not allow for a moredetailed description of the diffraction experiment. In many cases,data are collected at different wavelengths for phasing calculationsand structure refinement. For example, the data collected with achromium anode X-ray generator are generally not useful for therefinement due to resolution restrictions for long wavelengths, andthe use of longer wavelength may be underrepresented in PDBdeposits. As of September 2009, there are only 10 deposits collectedat synchrotrons and 9 at home sources that reported the use ofwavelengths longer than 2.0 A. The new practices applied at theCenter for Structural Genomics of Infectious Diseases (Anderson,2009), Seattle Structural Genomics Center for Infectious Diseases(Myler et al., 2009), and the Joint Center for Structural Genomics

0.5

R

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1.0 1.5 2.0 2.5 3.0 3.5Resolution (Å)

FIG. 7. R factor versus resolution for four PSI centers. Each center is representedby different color of data points and trend lines. (See Color Insert.)


(Lesley and Wilson, 2005) to provide raw diffraction imagestogether with a rudimentary description about how the structurewas solved. It has to be stressed that data collection is the lastexperiment in the structure determination process, which usuallycannot be repeated as crystals are typically destroyed during syn-chrotron experiment. Furthermore, repetition of data collectionis impossible if there are no more crystals of similar or better quality.

ACKNOWLEDGMENTS

The authors would like to thank Matthew Zimmerman, Zbyszek Dauter,Andrzej Joachimiak, and Wayne Anderson for valuable discussions. HepingZheng and Marek Grabowski helped with generating statistics. The workdescribed in the chapter was supported by GM74942, GM53163, and with Fed-eral funds from the National Institute of Allergy and Infectious Diseases, NationalInstitutes of Health, Department of Health and Human Services, under ContractNo. HHSN272200700058C.

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