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9 Protein Dynamics: From Structure to Function 249Tugarinov V, Shapiro YE, Liang Z, et al. (2002) A novel view of domain flexibility in E coli adenylatekinase based on structural mode-coupling 15 N NMR spin relaxation. J Mol Biol315:155–170Van Aalten DMF, Amadei A, Vriend G, et al. (1995a) The essential dynamics of thermolysin -confirmation of hinge-bending motion and comparison of simulations in vacuum and water.Prot Eng 8:1129–1136Van Aalten DMF, Findlay JBC, Amadei A, et al. (1995b) Essential dynamics of the cellular retinolbinding protein - evidence for ligand induced conformational changes. Prot Eng8:1129–1136Van Gunsteren WF, Berendsen HJC (1987) Groningen Molecular Simulation (GROMOS) LibraryManual. Biomos, GroningenVan Gunsteren WF, Berendsen HJC (1990) Computer-simulation of molecular-dynamics – methodology,applications, and perspectives in chemistry. Angew Chem Int Edit Engl29:992–1023Warshel A, Kato M, Pisliakov AV (2007) Polarizable force fields: history test cases and prospects.J Chem Theory Comput 3:2034–2045Weiner SJ, Kollman PA, Nguyen DT, et al. (1986) An all atom force field for simulations of proteinsand nucleic acids. J Comp Chem 7:230–252Weiss S (1999) Fluorescence spectroscopy of single biomolecules. Science 283:1676–1683Whitford PC, Miyashita O, Levy Y, et al. (2007) Conformational transitions of adenylate kinase:switching by cracking. J Mol Biol 366:1661–1671Xu Z, Horwich AL, Sigler PB (1997) The crystal structure of the asymmetric Gro-EL-GroES-(ADP) 7chaperonin complex. Nature 388:741–750Zachariae U, Grubmüller H (2006) A highly strained nuclear conformation of the exportin Cse1prevealed by molecular dynamics simulations. Structure 14:1469–1478Zhang X-J, Wozniak JA, Matthews BW (1995) Protein flexibility and adaptability seen in 25crystal forms of T4 lysozyme. J Mol Biol 250:527–552Zhang Z, Shi Y, Liu H (2003) Molecular dynamics simulations of peptides, and proteins withamplified collective motions. Biophys J 84:3583–3593Zheng W, Brooks BR (2005) Probing the local dynamics of nucleotide-binding pocket coupled tothe global dynamics: myosin versus kinesin. Biophys J 89(1):167–178Zheng W, Doniach S (2003) A comparative study of motor-protein motions by using a simpleelastic-network model. Proc Natl Acad Sci USA 100(23):13253–13258Zhou R Berne BJ, Germain R (2001) The free energy landscape for β-hairpin folding in explicitwater. Proc Natl Acad Sci USA 98:14931–14936
Chapter 10Integrated Servers for Structure-InformedFunction PredictionRoman A. LaskowskiAbstract No single method for predicting a protein’s function from its threedimensionalstructure is perfect; some methods work well in some cases, whereasother methods perform better in others. Consequently, it makes sense to applya number of different predictive methods to a given protein structure and obtaineither a consensus or the most likely prediction from them all. In this chapter wedescribe two web servers, ProKnow (http://proknow.mbi.ucla.edu) and ProFunc(http://www.ebi.ac.uk/profunc), that use a cocktail of methods for predicting functionfrom 3D structure.10.1 IntroductionPredicting the function of a newly solved protein structure is a bit like trying tosolve a tantalizing detective mystery. The 3D structure undoubtedly holds the cluesto what the protein does; but the problem is how to identify those clues, how toassess their reliability, how to spot and discard any red herrings, and how to piecethe remaining clues together to arrive at the final solution of the mystery.This problem has really only arisen fairly recently as a direct result of the variousStructural Genomics (SG) initiatives that were started up at the start of thisdecade. Before then, experimentalists would already know much about their proteinbefore embarking on determination of its 3D structure and would have selected itfor its biological interest. Much of the point of solving the structure was to gain aninsight into how the protein achieved its biological function at the atomic level. Themotivation of the SG groups, with their high-throughput structure determinationmethods, differed markedly. Now a protein would be solved if it belonged to a familywith no structural representatives, or was expected to have a novel fold, or was relevantto some disease. Knowledge of its function no longer came into it.R.A. LaskowskiEuropean Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge,CB10 1SD, UKe-mail: roman@ebi.ac.ukD.J. Rigden (ed.) From Protein Structure to Function with Bioinformatics, 251© Springer Science + Business Media B.V. 2009
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Daniel John RigdenEditorFrom Protei
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ContentsSection I Generating and In
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Contentsxi4.2.1 Alpha-Helical Bundl
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Contentsxiii7.4.2 Predicting Bindin
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Contentsxv12.3 Accuracy and Added V
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4 J. Lee et al.about 5.3 million pr
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6 J. Lee et al.Table 1.1 A list of
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8 J. Lee et al.2000; Sorin and Pand
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10 J. Lee et al.Although most knowl
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12 J. Lee et al.Fig. 1.3 Two exampl
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14 J. Lee et al.rugged containing m
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16 J. Lee et al.1.3.4 Mathematical
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18 J. Lee et al.potentials, each re
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20 J. Lee et al.viewpoint of struct
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22 J. Lee et al.Hsieh MJ, Luo R (20
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24 J. Lee et al.Sippl MJ (1990) Cal
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Chapter 2Fold RecognitionLawrence A
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2 Fold Recognition 29It has long be
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2 Fold Recognition 31Fig. 2.2 Graph
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2 Fold Recognition 33distribute the
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2 Fold Recognition 35Table 2.1 (a)
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2 Fold Recognition 37dependent on t
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2 Fold Recognition 39based on the r
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2 Fold Recognition 41The idea of co
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2 Fold Recognition 43query sequence
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2 Fold Recognition 452.3.4 Consensu
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2 Fold Recognition 472.4 Alignment
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2 Fold Recognition 49statistical me
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2 Fold Recognition 51methods (e.g.
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2 Fold Recognition 53Berman HM, Wes
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2 Fold Recognition 55Tress ML, Jone
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58 A. Fiserwith more than 50% seque
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60 A. FiserIn contrast to ab initio
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62 A. FiserTable 3.1 (continued)SWI
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64 A. Fiserbetween fold recognition
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66 A. FiserFig. 3.1 Comparing accur
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68 A. Fiserinto conserved core regi
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70 A. Fiseralignments are built and
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72 A. Fiserfold, such as the hyperv
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74 A. Fiserfunctions delivers impro
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76 A. Fiserfrom efforts of genome s
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78 A. FiserA rigorous statistical e
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80 A. Fiser(Clore et al. 1993). Cha
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82 A. FiserImproved and new methods
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84 A. FiserClaessens M, Van Cutsem
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86 A. FiserHavel TF, Snow ME (1991)
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88 A. FiserPetrey D, Xiang Z, Tang
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90 A. Fiservan Vlijmen HW, Karplus
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92 T. Nugent and D.T. Jones4.2 Stru
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94 T. Nugent and D.T. JonesFig. 4.2
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96 T. Nugent and D.T. JonesTable 4.
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98 T. Nugent and D.T. Jones2000), d
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100 T. Nugent and D.T. JonesTable 4
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102 T. Nugent and D.T. JonesFig. 4.
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104 T. Nugent and D.T. JonesFig. 4.
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106 T. Nugent and D.T. Jonessubdivi
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108 T. Nugent and D.T. Jonescomplex
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110 T. Nugent and D.T. JonesMartell
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Chapter 5Bioinformatics Approaches
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5 Structure and Function of Intrins
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Chapter 6Function Diversity Within
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6 Function Diversity Within Folds a
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Chapter 7Predicting Protein Functio
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7 Predicting Protein Function from
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Table 7.1 Online resources and tool
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Chapter 83D MotifsElaine C. Meng, B
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8 3D Motifs 189clustering similar s
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8 3D Motifs 191To improve the signa
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8 3D Motifs 193structures together.
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8 3D Motifs 195Table 8.1 (continued
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8 3D Motifs 1978.3.1 User-Defined M
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320 IndexConsensus templates, 205Co
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322 IndexLigand-binding templates,
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324 IndexStructure-function linkage
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