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256 R.A. LaskowskiFig. 10.2 Gene Ontology map generated for PDB entry 2fck showing the hierarchy of functionalterms, from the general to the specific. Where ProKnow identifies more than one functional predictionthe map returned will show a network of possibilities, each linked to any others that aresimilar, with the connections colour-coded by the similarity of the termsas the protein almost certainly comprises several structural domains, the boundariesof which would ideally be manually identified. Each domain’s fold has then to berecognized. Even if these stages are successful, the 3D arrangement of the domainsmay be crucial for the protein’s function, and methods to predict domain packingare in their infancy (Wollacott et al. 2007; Berrondo et al. 2008).10.2.2 3D MotifsAfter the fold-matching stage, the protein’s 3D structure is scanned for any 3Dmotifs that are strongly associated with function. These come from the RIGORdatabase of automatically generated structural motifs (Kleywegt 1999). Eachmotif consists of an ‘interesting’ arrangement of residues in a PDB structuralmodel. Three rules distinguish interesting arrangements from uninteresting ones:(a) the protein contains n sequential residues of the same type (e.g. four consecutivearginine residues), (b) a set of neighbouring residues are all hydrophobic, or
10 Integrated Servers for Structure-Informed Function Prediction 257Fig. 10.3 (a) The top ProKnow functional predictions for PDB entry 2fck. The top hit predicts,with high confidence, the protein to have N-acetyltransferase activity. (b) Master table of cluesused in each GO term prediction for 2fck. Clicking on any of the numbers in the table shows thedetails for the given clueall polar/charged, or a mixture of hydrophobic and polar/charged, and (c) residuesthat all make contact to a single hetero compound. ProKnow uses over 10,000RIGOR motifs; associated with each are the GO terms of the corresponding proteinchain.10.2.3 Sequence HomologyThe PSI-BLAST program (Altschul et al. 1997) is used for identifying proteins inthe UniProt/SWISS-PROT and UniProt/TrEMBL sequence databases that arehomologous to the target protein. Any matches that have GO annotations add theirfunctional clues to the pot.10.2.4 Sequence MotifsThe target protein’s sequence is then scanned for sequence motifs using thePROSITE database of functionally-associated motifs (Hulo et al. 2004). Again,each motif has a set of associated GO codes.
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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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Page 276 and 277: 270 R.A. LaskowskiReferencesAltschu
Page 278 and 279: 272 R.A. LaskowskiXenarios I, Salwi
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12 Prediction of Protein Function f
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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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