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272 R.A. LaskowskiXenarios I, Salwinski L, Duan XJ, et al. (2002) DIP, database of interacting proteins: aresearch tool for studying cellular networks of protein interactions. Nucleic Acids Res.30:303–305.Yakunin AF, Yee AA, Savchenko A, et al. (2004) Structural proteomics: a tool for genome annotation.Curr. Opin. Chem. Biol. 8:42–48.
Chapter 11Case Studies: Function Predictionsof Structural Genomics ResultsJames D. Watson and Janet M. ThorntonAbstract The development of high-throughput protein structure determinationpipelines by the various Structural Genomics initiatives around the globe hasresulted in the deposition of several thousand protein structures in the Protein DataBank. However, due to the nature of the target selection process and the requirementfor rapid data release, a significant proportion of these structures have little orno functional information. In order to address this problem a vast array of computationalmethods have been developed to predict a protein’s function from its threedimensional structure. The approaches range from large scale fold comparison tohighly specific residue templates, each with its own advantages and disadvantages.Here we look at the application of these methods in Structural Genomics andreview attempts to determine how successful function prediction from structure hasbeen, with specific examples illustrating some of the success stories.11.1 IntroductionGenome sequencing projects around the globe have already provided enormousamounts of data on the genes that are essential to a number of organisms, and thisinformation is expanding rapidly with the large-scale metagenomics projects currentlyunder way (Yooseph et al. 2007). By comparison, the amount of proteinstructure data available lags far behind. Structural genomics aims to bridge thisgap by solving, in a high-throughput manner, a large number of novel structuresthat can be used to model a larger number of sequences (Fox et al. 2008; Service2005). A consequence of this approach has been the deposition of large numbersof structures with little or no functional annotation. This is in direct contrast totraditional structural biology where the function of a protein is often known inadvance and the structure is often solved to identify the biochemical mechanismsand unique subtleties of its action.J.D. Watson and J.M. Thornton*European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton,Cambridgeshire, CB10 1SD, UK*Corresponding author: e-mail: thornton@ebi.ac.ukD.J. Rigden (ed.) From Protein Structure to Function with Bioinformatics, 273© 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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8 3D Motifs 199Fig. 8.3 The FFF mot
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8 3D Motifs 2018.3.2 Motif Discover
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8 3D Motifs 203In addition to SITE
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8 3D Motifs 205folds; they shared a
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8 3D Motifs 207from a training set
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8 3D Motifs 209Table 8.3 Web server
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8 3D Motifs 211its greater overall
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8 3D Motifs 213Ideally, a 3D motif
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8 3D Motifs 215Ivanisenko VA, Pintu
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Chapter 9Protein Dynamics: From Str
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9 Protein Dynamics: From Structure
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324 IndexStructure-function linkage
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