Journal of Computer-Aided Molecular Design

, Volume 29, Issue 9, pp 817–836 | Cite as

Models of protein–ligand crystal structures: trust, but verify

  • Marc C. Deller
  • Bernhard RuppEmail author


X-ray crystallography provides the most accurate models of protein–ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein–ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein–ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein–ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein–ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein–ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein–ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.


Crystal structure Protein structure Protein–ligand complex Quality control Structure validation Structure-based drug design 



MCD acknowledges support from the NIH, National Institute of General Medical Sciences, Protein Structure Initiative under Grant Number U54 GM094586. BR acknowledges support from the European Union under a FP7 Marie Curie People Action, Grant PIIF-GA-2011–300025 (SAXCESS).


  1. 1.
    Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD et al (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542CrossRefGoogle Scholar
  2. 2.
    Berman H (2008) The Protein Data Bank: a historical perspective. Acta Crystallogr A 64:88–95CrossRefGoogle Scholar
  3. 3.
    Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide Protein Data Bank. Nat Struct Biol 10:980CrossRefGoogle Scholar
  4. 4.
    Gutmanas A, Alhroub Y, Battle GM, Berrisford JM, Bochet E et al (2014) PDBe: protein Data Bank in Europe. Nucleic Acids Res 42:D285–D291CrossRefGoogle Scholar
  5. 5.
    Henderson R, Sali A, Baker ML, Carragher B, Devkota B et al (2012) Outcome of the first electron microscopy validation task force meeting. Structure 20:205–214CrossRefGoogle Scholar
  6. 6.
    Dutta S, Burkhardt K, Swaminathan GJ, Kosada T, Henrick K et al (2008) Data deposition and annotation at the Worldwide Protein Data Bank. In: Kobe B, Guss M, Huber T (eds) Structural proteomics: high-throughput methods. Humana Press/Springer, New York, NYGoogle Scholar
  7. 7.
    Carvalho AL, Trincao J, Romao MJ (2009) X-ray crystallography in drug discovery. Methods Mol Biol 572:31–56CrossRefGoogle Scholar
  8. 8.
    Zheng H, Hou J, Zimmerman MD, Wlodawer A, Minor W (2014) The future of crystallography in drug discovery. Expert Opin Drug Discov 9:125–137CrossRefGoogle Scholar
  9. 9.
    Davis AM, St-Gallay SA, Kleywegt GJ (2008) Limitations and lessons in the use of X-ray structural information in drug design. Drug Discov Today 13:831–841CrossRefGoogle Scholar
  10. 10.
    Krishnan VV, Rupp B (2012) Macromolecular structure determination: comparison of X-ray crystallography and NMR. Spectroscopy. eLS. doi: 10.1002/9780470015902.a9780470002716.pub9780470015902 Google Scholar
  11. 11.
    Davies TG, Tickle IJ (2012) Fragment screening using X-ray crystallography. Top Curr Chem 317:33–59CrossRefGoogle Scholar
  12. 12.
    Pozharski E, Weichenberger CX, Rupp B (2013) Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures. Acta Crystallogr D 69:150–167CrossRefGoogle Scholar
  13. 13.
    Kleywegt GJ, Harris MR (2007) ValLigURL: a server for ligand-structure comparison and validation. Acta Crystallogr 63:935–938Google Scholar
  14. 14.
    Cereto-Massague A, Ojeda MJ, Joosten RP, Valls C, Mulero M et al (2013) The good, the bad and the dubious: VHELIBS, a validation helper for ligands and binding sites. J Cheminform 5:36CrossRefGoogle Scholar
  15. 15.
    Weichenberger CX, Pozharski E, Rupp B (2013) Visualizing ligand molecules in twilight electron density. Acta Crystallogr F69:195–200Google Scholar
  16. 16.
    Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WT et al (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 50:726–741CrossRefGoogle Scholar
  17. 17.
    Warren GL, Do TD, Kelley BP, Nicholls A, Warren SD (2012) Essential considerations for using protein-ligand structures in drug discovery. Drug Discov Today 17:1270–1281CrossRefGoogle Scholar
  18. 18.
    Hawkins PCD, Warren GL, Skillman AG, Nicholls A (2008) How to do an evaluation: pitfalls and traps. J Comput Aided Mol Des 22:179–190CrossRefGoogle Scholar
  19. 19.
    Westbrook JD, Fitzgerald PM (2003) The PDB format, mmCIF, and other data formats. Methods Biochem Anal 44:161–179Google Scholar
  20. 20.
    Kleywegt GJ, Harris MR, Zou JY, Taylor TC, Wahlby A et al (2004) The uppsala electron-density server. Acta Crystallogr D60:2240–2249Google Scholar
  21. 21.
    Joosten RP, Joosten K, Murshudov GN, Perrakis A (2012) PDB_REDO: constructive validation, more than just looking for errors. Acta Crystallogr D 68:484–496CrossRefGoogle Scholar
  22. 22.
    Joosten RP, Long F, Murshudov GN, Perrakis A (2014) The PDB_REDO server for macromolecular structure model optimization. IUCrJ 1:213–220CrossRefGoogle Scholar
  23. 23.
    Rhodes G (2006) Crystallography made crystal clear. Academic Press, London, UKGoogle Scholar
  24. 24.
    Rupp B (2009) Biomolecular crystallography: principles, practice, and application to structural biology. Garland Science, New YorkGoogle Scholar
  25. 25.
    Elsliger MA, Deacon AM, Godzik A, Lesley SA, Wooley J et al (2010) The JCSG high-throughput structural biology pipeline. Acta Crystallogr F66:1137–1142Google Scholar
  26. 26.
    Weichenberger CX, Rupp B (2014) Ten years of probabilistic estimates of biocrystal solvent content: new insights via nonparametric kernel density estimate. Acta Crystallogr D 70:1579–1588CrossRefGoogle Scholar
  27. 27.
    Debreczeni JE, Emsley P (2012) Handling ligands with coot. Acta Crystallogr D68:425–430Google Scholar
  28. 28.
    Emsley P, Lohkamp B, Scott WG, Cowtan K (2010) Features and development of coot. Acta Crystallogr D 66:486–501CrossRefGoogle Scholar
  29. 29.
    Krissinel E (2010) Crystal contacts as nature’s docking solutions. J Comput Chem 31:133–143CrossRefGoogle Scholar
  30. 30.
    Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372:774–797CrossRefGoogle Scholar
  31. 31.
    Danley D (2006) Crystallization to obtain protein-ligand complexes for structure-aided drug design. Acta Crystallogr D 62:569–575CrossRefGoogle Scholar
  32. 32.
    Muller Y (2013) Unexpected features in the Protein Data Bank entries 3qd1 and 4i8e: the structural description of the binding of the serine-rich repeat adhesin GspB to host cell carbohydrate receptor is not a solved issue. Acta Crystallogr F69:1071–1076Google Scholar
  33. 33.
    Tronrud D, Allen J (2012) Reinterpretation of the electron density at the site of the eighth bacteriochlorophyll in the FMO protein from Pelodictyon phaeum. Photosynthesis Res 112:71–74CrossRefGoogle Scholar
  34. 34.
    Gokulan K, Khare S, Ronning D, Linthicum SD, Sacchettini JC et al (2005) Co-crystal structures of NC6.8 Fab identify key interactions for high potency sweetener recognition: implications for the design of synthetic sweeteners. Biochemistry 44:9889–9898CrossRefGoogle Scholar
  35. 35.
    Engh RA, Huber R (1991) Accurate bond and angle parameters for X-ray protein structure refinement. Acta Crystallogr A 47:392–400CrossRefGoogle Scholar
  36. 36.
    Engh RA, Huber R (2001) In: Arnold MGRE (ed) International tables for crystallography. Kluwer, Dordrecht, pp 382–392Google Scholar
  37. 37.
    Kleywegt GJ (2007) Crystallographic refinement of ligand complexes. Acta Crystallogr D 63:94–100CrossRefGoogle Scholar
  38. 38.
    Brunger AT (1992) Free R value: a novel statistical quantity for assessing the accuracy of crystal structures. Nature 355:472–475CrossRefGoogle Scholar
  39. 39.
    Karplus PA, Diederichs K (2012) Linking crystallographic model and data quality. Science 336:1030–1033CrossRefGoogle Scholar
  40. 40.
    Tickle IJ (2012) Statistical quality indicators for electron-density maps. Acta Crystallogr D 68:454–467CrossRefGoogle Scholar
  41. 41.
    Read RJ (1986) Improved Fourier coefficients for maps using phases from partial structures with errors. Acta Crystallogr A 42:140–149CrossRefGoogle Scholar
  42. 42.
    Hodel A, Kim S-H, Brunger AT (1992) Model bias in macromolecular structures. Acta Crystallogr D 48:851–858CrossRefGoogle Scholar
  43. 43.
    Branden C-I, Alwyn Jones T (1990) Between objectivity and subjectivity. Nature 343:687–689CrossRefGoogle Scholar
  44. 44.
    Jones TA, Zou JY, Cowan SW, Kjeldgaard M (1991) Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Crystallogr A 47:110–119CrossRefGoogle Scholar
  45. 45.
    Read Randy J, Adams Paul D, Arendall Iii WB, Brunger Axel T, Emsley P et al (2011) A new generation of crystallographic validation tools for the protein data bank. Structure 19:1395–1412CrossRefGoogle Scholar
  46. 46.
    Rupp B, Segelke BW (2001) Questions about the structure of the botulinum neurotoxin B light chain in complex with a target peptide. Nat Struct Biol 8:643–664CrossRefGoogle Scholar
  47. 47.
    Hanson MA, Oost TK, Sukonpan C, Rich DH, Stevens RC (2002) Structural basis for BABIM inhibition of botulinum neurotoxin type B protease. J Am Chem Soc 124:10248CrossRefGoogle Scholar
  48. 48.
    Hanson MA, Stevens RC (2009) Retraction: cocrystal structure of synaptobrevin-II bound to botulinum neurotoxin type B at 2.0 A resolution. Nat Struct Mol Biol 16:795CrossRefGoogle Scholar
  49. 49.
    Rupp B (2008) Scientific inquiry and inference in macromolecular crystallography. Acta Crystallogr A 64:C81CrossRefGoogle Scholar
  50. 50.
    Vilcheze C, Wang F, Arai M, Hazbon MH, Colangeli R et al (2006) Transfer of a point mutation in Mycobacterium tuberculosis inhA resolves the target of isoniazid. Nat Med 12:1027–1029CrossRefGoogle Scholar
  51. 51.
    Allen FH (2002) The Cambridge structural database: a quarter of a million crystal structures and rising. Acta Crystallogr B pp 380–388Google Scholar
  52. 52.
    Bruno IJ, Cole JC, Kessler M, Luo J, Motherwell WDS et al (2004) Retrieval of crystallographically-derived molecular geometry information. J Chem Inf Comput Sci 44:2133–2144CrossRefGoogle Scholar
  53. 53.
    Chen VB, Arendall WB III, Headd JJ, Keedy DA, Immormino RM et al (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D 66:12–21CrossRefGoogle Scholar
  54. 54.
    Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ et al (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35:W375–W383CrossRefGoogle Scholar
  55. 55.
    Hooft RW, Vriend G, Sander C, Abola EE (1996) Errors in protein structures. Nature 381:272CrossRefGoogle Scholar
  56. 56.
    Kleywegt GJ, Jones TA (1998) Databases in protein crystallography. Acta Crystallogr D54:1119–1131Google Scholar
  57. 57.
    van Aalten DM, Bywater R, Findlay JB, Hendlich M, Hooft RW et al (1996) PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules. J Comput Aided Mol Des 10:255–262CrossRefGoogle Scholar
  58. 58.
    Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Methodol 3:537–547CrossRefGoogle Scholar
  59. 59.
    Clowney L, Westbrook JD, Berman HM (1999) CIF applications. XI. A la mode: a ligand and monomer object data environment. I. Automated construction of mmCIF monomer and ligand models. Appl Cryst 32:125–133CrossRefGoogle Scholar
  60. 60.
    Peat TS, Christopher J, Schmidt K (2005) AFITT- working with good chemistry. Acta Crystallogr A 61:C165CrossRefGoogle Scholar
  61. 61.
    Golovin A, Oldfield TJ, Tate JG, Velankar S, Barton GJ et al (2004) E-MSD: an integrated data resource for bioinformatics. Nucleic Acids Res 32:D211–D216CrossRefGoogle Scholar
  62. 62.
    Chen J, Swamidass SJ, Dou Y, Bruand J, Baldi P (2005) ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21:4133–4139CrossRefGoogle Scholar
  63. 63.
    Garavelli JS (2004) The RESID database of protein modifications as a resource and annotation tool. Proteomics 4:1527–1533CrossRefGoogle Scholar
  64. 64.
    Bohne A, Lang E, von der Lieth CW (1999) SWEET: WWW-based rapid 3D construction of oligo- and polysaccharides. Bioinformatics 15:767–768CrossRefGoogle Scholar
  65. 65.
    Nilsson K, Lecerof D, Sigfridsson E, Ryde U (2003) An automatic method to generate force-field parameters for hetero-compounds. Acta Crystallogr D 59:274–289CrossRefGoogle Scholar
  66. 66.
    Feng Z, Chen L, Maddula H, Akcan O, Oughtred R et al (2004) Ligand depot: a data warehouse for ligands bound to macromolecules. Bioinformatics 20:2153–2155CrossRefGoogle Scholar
  67. 67.
    Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182CrossRefGoogle Scholar
  68. 68.
    Hendlich M, Bergner A, Günther J, Klebe G (2003) Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. J Mol Biol 326:607–620CrossRefGoogle Scholar
  69. 69.
    Andrejasic M, Praaenikar J, Turk D (2008) PURY: a database of geometric restraints of hetero compounds for refinement in complexes with macromolecular structures. Acta Crystallogr D 64:1093–1109CrossRefGoogle Scholar
  70. 70.
    Sehnal D, Svobodová Vařeková R, Pravda L, Ionescu C-M, Geidl S, et al (2015) ValidatorDB: database of up-to-date validation results for ligands and non-standard residues from the Protein Data Bank. Nucleic Acids Res 43:D369–D375Google Scholar
  71. 71.
    Varekova RS, Jaiswal D, Sehnal D, Ionescu CM, Geidl S et al (2014) MotiveValidator: interactive web-based validation of ligand and residue structure in biomolecular complexes. Nucleic Acids Res 42:W227–W233CrossRefGoogle Scholar
  72. 72.
    Hartshorn MJ (2002) AstexViewer: a visualisation aid for structure-based drug design. J Comput Aided Mol Des 16:871–881CrossRefGoogle Scholar
  73. 73.
    Henrick K, Feng Z, Bluhm WF, Dimitropoulos D, Doreleijers JF et al (2008) Remediation of the protein data bank archive. Nucleic Acids Res 36:D426–D433CrossRefGoogle Scholar
  74. 74.
    Jaskolski M (2013) On the propagation of errors. Acta Crystallogr D 69:1865–1866CrossRefGoogle Scholar
  75. 75.
    Langer G, Cohen SX, Lamzin VS, Perrakis A (2008) Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7. Nat Protoc 3:1171–1179CrossRefGoogle Scholar
  76. 76.
    Terwilliger T (2004) SOLVE and RESOLVE: automated structure solution, density modification and model building. J Synchrotron Radiat 11:49–52CrossRefGoogle Scholar
  77. 77.
    Cowtan K (2012) Completion of autobuilt protein models using a database of protein fragments. Acta Crystallogr D 68:328–335CrossRefGoogle Scholar
  78. 78.
    Weichenberger CX, Sippl MJ (2007) NQ-Flipper: recognition and correction of erroneous asparagine and glutamine side-chain rotamers in protein structures. Nucleic Acids Res 35:W403–W406CrossRefGoogle Scholar
  79. 79.
    Carolan CG, Lamzin VS (2014) Automated identification of crystallographic ligands using sparse-density representations. Acta Crystallogr D 70:1844–1853CrossRefGoogle Scholar
  80. 80.
    Terwilliger TC, Adams PD, Moriarty NW, Cohn JD (2007) Ligand identification using electron-density map correlations. Acta Crystallogr D 63:101–107CrossRefGoogle Scholar
  81. 81.
    Aishima J, Russel DS, Guibas LJ, Adams PD, Brunger AT (2005) Automated crystallographic ligand building using the medial axis transform of an electron-density isosurface. Acta Crystallogr D 61:1354–1363CrossRefGoogle Scholar
  82. 82.
    Evrard GX, Langer GG, Perrakis A, Lamzin VS (2007) Assessment of automatic ligand building in ARP/wARP. Acta Crystallogr D 63:108–117CrossRefGoogle Scholar
  83. 83.
    Wlodek S, Skillman AG, Nicholls A (2006) Automated ligand placement and refinement with a combined force field and shape potential. Acta Crystallogr D 62:741–749CrossRefGoogle Scholar
  84. 84.
    Klei HE, Moriarty NW, Echols N, Terwilliger TC, Baldwin ET et al (2014) Ligand placement based on prior structures: the guided ligand-replacement method. Acta Crystallogr D 70:134–143CrossRefGoogle Scholar
  85. 85.
    Laskowski RA, Swindells MB (2011) LigPlot + : multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786CrossRefGoogle Scholar
  86. 86.
    Kleywegt GJ (2000) Validation of protein crystal structures. Acta Crystallogr D 56:249–265CrossRefGoogle Scholar
  87. 87.
    Dauter Z, Wlodawer A, Minor W, Jaskolski M, Rupp B (2014) Avoidable errors in deposited macromolecular structures: an impediment to efficient data mining. IUCrJ 1:179–193CrossRefGoogle Scholar
  88. 88.
    Liebeschuetz J, Hennemann J, Olsson T, Groom CR (2012) The good, the bad and the twisted: a survey of ligand geometry in protein crystal structures. J Comput Aided Mol Des 26:169–183CrossRefGoogle Scholar
  89. 89.
    Baker E, Dauter Z, Guss M, Einspahr H (2008) Deposition of diffraction images to be discussed at the Open Meeting of the Commission on Biological Macromolecules of the IUCr in Osaka. Acta Crystallogr F64:231–232Google Scholar
  90. 90.
    Cruickshank DW (1999) Remarks about protein structure precision. Acta Crystallogr D 55:583–601CrossRefGoogle Scholar
  91. 91.
    Laskowski RA, Macarthur MW, Moss DS, Thornton JM (1993) {PROCHECK}: a program to check the stereochemical quality of protein structures. Appl Cryst 26:283–291CrossRefGoogle Scholar
  92. 92.
    Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99CrossRefGoogle Scholar
  93. 93.
    Sheffler W, Baker D (2009) RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Sci 18:229–239Google Scholar
  94. 94.
    Sheffler W, Baker D (2010) RosettaHoles2: a volumetric packing measure for protein structure refinement and validation. Protein Sci 19:1991–1995CrossRefGoogle Scholar
  95. 95.
    Debye P (1913) Interferenz von Röntgenstrahlen und Wärmebewegung. Ann Phys 348:49–92CrossRefGoogle Scholar
  96. 96.
    Waller I (1923) Zur Frage der Einwirkung der Wärmebewegung auf die Interferenz von Röntgenstrahlen. Zeitschrift für Physik 17:398–408CrossRefGoogle Scholar
  97. 97.
    Lutteke T, Frank M, von der Lieth CW (2005) Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB. Nucleic Acids Res 33:D242–D246CrossRefGoogle Scholar
  98. 98.
    Lutteke T, von der Lieth CW (2004) pdb-care (PDB carbohydrate residue check): a program to support annotation of complex carbohydrate structures in PDB files. BMC Bioinformatics 5:69CrossRefGoogle Scholar
  99. 99.
    Collaborative Computational Project, Number 4 (1994) Acta Cryst D50:760–763.
  100. 100.
    Smart OS, Womack TO, Flensburg C, Keller P, Paciorek W et al (2012) Exploiting structure similarity in refinement: automated NCS and target-structure restraints in BUSTER. Acta Crystallogr D 68:368–380CrossRefGoogle Scholar
  101. 101.
    Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8(52–56):29Google Scholar
  102. 102.
    Adams PD, Afonine PV, Bunkoczi G, Chen VB, Davis IW et al (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D 66:213–221CrossRefGoogle Scholar
  103. 103.
    Vaguine AA, Richelle J, Wodak SJ (1999) SFCHECK: a unified set of procedures for evaluating the quality of macromolecular structure-factor data and their agreement with the atomic model. Acta Crystallogr D 55:191–205CrossRefGoogle Scholar
  104. 104.
    Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85CrossRefGoogle Scholar
  105. 105.
    Urzhumtseva L, Afonine PV, Adams PD, Urzhumtsev A (2009) Crystallographic model quality at a glance. Acta Crystallogr D 65:297–300CrossRefGoogle Scholar
  106. 106.
    Bhattacharya A, Tejero R, Montelione GT (2007) Evaluating protein structures determined by structural genomics consortia. Proteins 66:778–795CrossRefGoogle Scholar
  107. 107.
    Sippl MJ (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 17:355–362CrossRefGoogle Scholar
  108. 108.
    Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410CrossRefGoogle Scholar
  109. 109.
    Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519CrossRefGoogle Scholar
  110. 110.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN et al (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Joint Center for Structural GenomicsSan DiegoUSA
  2. 2.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  3. 3.VistaUSA
  4. 4.Department of Genetic EpidemiologyMedical University of InnsbruckInnsbruckAustria

Personalised recommendations