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268 R.A. LaskowskiFig. 10.8 Example analyses from the PDBsum pages generated when any structure is submittedto ProFunc. (a) A schematic diagram of the protein chain showing the protein’s secondary structureelements (α-helices and β-sheets) together various structural motifs such as β- and γ-turns,and β-hairpins. In this example residues interacting with bound ligands are indicated by the reddots above the single-letter amino acid code. In the 2fck structure the ligands are not particularly
10 Integrated Servers for Structure-Informed Function Prediction 269fold comparison method, such as SSM. However, far from being redundant, thereverse template method provides much more specific information about thesimilarities between any two structures. Furthermore, it identifies the regionssharing the highest similarity and consequently most likely to be the functionalsite. Also, it provides stronger confirmatory evidence for a putative functionshowing, as it does, the key residues involved.Of course, the only true way of validating a prediction is to confirm it experimentally.This is difficult, time-consuming and expensive although some progresshas been made towards development of high-throughput functional assays (Yakuninet al. 2004).10.4 ConclusionHere we have described ProKnow and ProFunc, two integrated servers that use acombination of structure- and sequence-based matching methods to try to predictthe function of a protein from an uploaded 3D structural model. In most cases, theyare able to offer some suggestions about possible function, although these maysometimes be rather vague (e.g. DNA-binding activity). In other cases, however, alltheir methods draw a blank and they fail completely. The most challenging casesare structures belonging to uncharacterized protein families possessing a novel fold.So, all one may be left with is the knowledge that the structure has an interestinglookingcleft in its surface, lined by highly conserved residues, but with no hint ofwhat might bind there. For cases such as these, new methods need to be developedand incorporated into the servers. Of most utility would be methods that can predictwhat a given protein’s likely substrate is from an analysis of the structure alone.That is, the methods should not rely on a match to an existing structure as, for novelfolds, there is by definition no such match. At present, such methods are very computeintensiveand tend to commence with some idea of class of substrate at least(e.g. Hermann et al. 2006). So, for the time being, prediction of a protein’s functionwill continue to rely on clever sleuthing and deduction.Acknowledgement The author would like to thank Debnath Pal for help with ProKnow and forhis useful comments on this chapter.Fig. 10.8 (continued) interesting or functionally informative, deriving from elements of the crystallizationsolution, and comprise 12 nitrate ions and one molecule of glycerol. (b) Topologydiagram of the 2fck protein chain. The diagram illustrates how the β-strands, represented by thelarge pink arrows, join up, side-by-side, to form the domain’s central β-sheet. The diagram alsoshows the relative locations of the α-helices, here represented by the red cylinders. The small bluearrows indicate the directionality of the protein chain, from the N- to the C-terminus. The numberswithin the secondary structural elements correspond to the residue numbering given in the PDB file.The diagram is generated from the output of the Hera program (Hutchinson and Thornton 1990)
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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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Page 258 and 259: 252 R.A. LaskowskiConsequently, man
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Page 268 and 269: 262 R.A. Laskowskiaccessibility and
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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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320 IndexConsensus templates, 205Co
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322 IndexLigand-binding templates,
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324 IndexStructure-function linkage
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