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288 J.D. Watson and J.M. ThorntonPDB has its own page automatically seeded using information retrieved from OCA(Prilusky 1996) and other sources. There are a number of key difference inProteopedia compared to PDBWiki, the greatest of which is its unique linking oftext with scenes in a Jmol viewer (rather than a static image of the protein,Proteopedia provides a fully interactive Jmol view of the entry). Using a sceneauthor system, anyone editing a page can easily create scenes to highlight keyregions of a protein or restrict the view to the region being discussed in the text.This makes the Proteopedia resource more than just a set of static pages to displayinformation, instead providing an extra layer of interaction that can be used tomore easily demonstrate ideas and illustrate regions of interest. The site is alsounique in that there are no anonymous edits and the user’s full name is credited ascontributing to the page. This approach has one additional advantage: users canhave their own visible but non-editable areas in the system. This allows for thegeneration of topic-based or example pages that remain stable and can thereforebe used as a teaching tool as well as a community annotation tool.11.5 ConclusionsThere have been a great number of structures deposited by the Structural Genomicsgroups which have contributed to our treasury of protein 3D structures. However,due to the rapid data release required by these projects a large proportion of structureshave little or no functional information. The ability to predict a protein’s functionfrom sequence and structure has been something of a Holy Grail forbioinformaticians and consequently a wide variety of methods have been developedover the years. Each of these methods has its own pros and cons and there arenumerous individual case studies highlighting how one particular method can providebiological insight where others failed. No single method at this time shows a100% success rate and therefore the continued development of novel structuralanalysis and function prediction tools is essential to helping our understanding.There have been a few attempts to perform large-scale analyses of the ability topredict protein function from structure with some success but there are a few keyproblems that need to be addressed in this field. One of the greatest problems facedwhen developing function prediction methods from structure is that there is no real“gold standard” dataset to test on. As a result each method developer has had topick their own data to test on, resulting in difficulties when comparing differentmethods to one another. The second major hurdle that needs to be addressed is theproblem of comparing structure-based approaches against sequence-based methods.Although not impossible in all cases, the ability to remove information fromthe sequence databases and the patterns and profiles constructed using them is amuch more difficult problem. This only applies to “after the event” types of analysis(like that performed by Watson et al. 2007). If, instead, all function predictionswere made on the day of the structure’s release, to be stored and analysed at a future date,this problem would be overcome. This process has already been started using the
11 Function Predictions of Structural Genomics Results 289ProFunc server with results on all MCSG structures being stored for future analysisand comparison. Neither of these issues is trivial and will require careful constructionif the datasets are to be used in future to assess and compare methods.In order to maximise the chances of detecting the correct function, there hasbeen a move towards the development of combinatorial methods, which aim to utiliseas much information as possible. The integration of existing bioinformaticstechniques has been in direct response to the large numbers of proteins beingreleased with little or no functional annotation. One of the great challenges facingprotein function prediction is the incorporation of data and analyses from all areasof biological sciences, and as the amount of information available rises the recentmoves towards community-wide annotation projects are likely to be pivotal in helpingprovide deeper biological insights.Acknowledgements The authors would like to thank Roman Laskowski and Vicky Schneider fortheir useful comments on this chapter.ReferencesAdams MA, Jia Z (2006) Modulator of Drug Activity B from Escherichia coli: crystal structureof a prokaryotic homologue of DT-diaphorase. J Mol Biol 359:455–465Adams MA, Suits MD, Zheng J, et al. (2007) Piecing together the structure-function puzzle:experiences in structure-based functional annotation of hypothetical proteins. Proteomics7:2920–2932Aravind L, Anantharaman V, Balaji S, et al. (2005) The many faces of the helix-turn-helix domain:transcription regulation and beyond. FEMS Microbiol Rev 29:231–262Binkowski, TA, Freeman P, Liang J (2004) pvSOAR: detecting similar surface patterns of pocketand void surfaces of amino acid residues on proteins. Nucleic Acids Res 32: W555–W558Boocock GR, Morrison JA, Popovic M, et al. (2003) Mutations in SBDS are associated withShwachman-Diamond syndrome. Nat Genet 33:97–101Fox BG, Goulding C, Malkowski MG, et al. (2008) Structural genomics: from genes to structureswith valuable materials and many questions in between. Nat Methods 5:129–132Giles J (2007) Key biology databases go wiki. Nature 445: 691Glaser F, Morris RJ, Najmanovich RJ, et al. (2006) A method for localizing ligand binding pocketsin protein structures. Proteins 62:479–488Hermann JC, Marti-Arbona R, Fedorov AA, et al. (2007) Structure-based activity prediction foran enzyme of unknown function. Nature 448:775–779Holm L, Sander C (1995) Dali: a network tool for protein structure comparison. Trends BiochemSci 20:478–480Hsu SK, Lin LL, Lo HH, et al. (2004) Mutational analysis of feedback inhibition and catalyticsites of prephenate dehydratase from Corynebacterium glutamicum. Arch Microbiol181:237–244Huang L, Hung LW, Odell M, et al. (2002) Structure-based experimental confirmation of biochemicalfunction to a methyltransferase, MJ0882, from hyperthermophile Methanococcusjannaschii. J Struct Funct Genomics 2:121–127Hwang KY, Chung JH, Kim S-H, et al. (1999) Structure-based identification of a novel NTPasefrom Methanococcus jannaschii. Nat Struct Biol 6:691–696Kim KK, Kim R, Kim S-H (1998) Crystal structure of a small heat shock protein. Nature394:595–599
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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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