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12 Prediction of Protein Function from Theoretical Models 299Table 12.2 The accuracy and added value of structure-derived properties in single-templatebased comparative models (Chakravarty and Sanchez 2004; Chakravarty et al. 2005)Model-derived property Accuracy Added valueAllIncreases with template-to-targetidentityIncreases when templateto-targetidentity dropsResidue exposure state Decreases with the protein size;affected by alignment errorsbelow 30% sequence identityNo added valueBuried residuesneighbourhoodExposed residuesneighbourhoodAccessible surface area (ASA)Surface pockets identificationSurface pockets compositionElectrostatic potential (EP)No clear dependence on proteinsize, higher than for exposedresidues; affected by alignmenterrors below 30%sequence identityNo clear dependence on proteinsize, lower than for buriedresidues; affected by alignmenterrors below 30%sequence identityError in total ASA increaseswith protein size, influenceof misalignment is verysmallPocket artefacts; increasednumber of surface pockets incomparison to the templateand target structure; alignmenterrors have no cleareffect on the number ofpocketsAffected by alignment errorsbelow 50% sequence identityNo added valueModerate added valueModerate added valueNegative added valueHigh added valueHigh added valuewhen the sequence identity drops below 50% (Table 12.2). It has to be stressed thatthe analysis was performed on the basis of the set of comparative single-templatemodels where no loop modelling was performed. Thus the results are representativeof the models produced by large-scale fully automated methods. More elaboratemodelling procedures consisting of the use of multiple templates and refinementmay provide improved models and may increase accuracy of the predictions ofparticular properties for a given sequence similarity.Knowing how reliable the different model-derived properties are, it is interestingto investigate what additional information (added value) they carry with referenceto the template structures used for model building. Again, systematic analysis of themodel added value was performed on the large scale only for single-template models(Chakravarty and Sanchez 2004), but it provides valuable guidelines as to whichparticular model-derived properties can be informative (Table 12.2).In general, the greater the difference between target and template sequences, themore significant the added value becomes. This results from the fact that lower-simi-
300 I.A. Cymerman et al.larity cases contain less information in the template about the size and physicochemicalproperties of particular residues in the target. However, not all structure-derivedproperties provide additional information with respect to the template. For SDPs thatdepend mostly on position of residues, such as exposure state, neighbourhood of buriedresidues and number of surface pockets, models do not provide added-value. It isprobably caused by the fact that buried residues are more conserved than exposedresidues, comprising protein cores that are responsible for protein integrity.For other SDPs, such as neighbourhood of exposed residues and total accessiblesurface area (ASA), models show some added-value. This is very important as residuesaccessible to the solvent are responsible for interactions with other molecules,thus determining the biological function of the protein.Finally, for properties that strongly depend on the physicochemical characteristicsof the amino acids in the sequence, such as composition of pockets andelectrostatic potential, models show large added-value. The identification ofcharged regions is of large value as they may represent binding or active sites (seeChapter 7).To summarize: generally the studies performed by Chakravarty et al. demonstratethat, with the exception of the detection of pockets, most model-derivedstructural properties exhibit some level of added-value. The more a given propertydepends on the sequence of the protein the more useful a model will be in estimatingthe value of that property. Encouragingly, depending on the feature, 25–40%sequence identity between target and template was sufficient to produce a SDPestimate of comparable accuracy to that available from an NMR structure.12.3.1 ImplementationThe knowledge about the added-value of particular structural and surface propertiesof models raises the question whether they can be also useful for the functionprediction. In 1998 Fetrow and Skolnick proposed a multi-step procedure thatenables identification of protein functional sites in low-to-moderate resolutionmodels (Fetrow and Skolnick 1998). Based on geometry, residue identity, distancesbetween alpha carbons and conformation, the active site residues become athree dimensional descriptor termed Fuzzy Functional Form (FFF). AfterwardsFFFs are used to screen the set of three dimensional models to identify withinthem those containing similar structural motifs. The usefulness of the method wasproved by the identification of the novel members of the disulphide glutaredoxin/thioredoxin protein family within in the yeast (Fetrow and Skolnick 1998) and E.coli genomes (Fetrow et al. 1998), whose function could not be identified bysequence comparison methods. The great advantage of FFF and related approachesis that the method distinguishes protein pairs with similar active sites fromproteins pairs that may have similar folds, but not necessarily similar active sites.The FFF technology was further developed to the method called active siteprofiling (Cammer et al. 2003) and was successfully combined with experimental
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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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Page 258 and 259: 252 R.A. LaskowskiConsequently, man
Page 260 and 261: 254 R.A. Laskowskiucla.edu, and Pro
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Page 266 and 267: 260 R.A. LaskowskiFig. 10.4 Schemat
Page 268 and 269: 262 R.A. Laskowskiaccessibility and
Page 270 and 271: 264 R.A. LaskowskiFig. 10.6 A ligan
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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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Page 298 and 299: Chapter 12Prediction of Protein Fun
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Page 324 and 325: 320 IndexConsensus templates, 205Co
Page 326 and 327: 322 IndexLigand-binding templates,
Page 328: 324 IndexStructure-function linkage
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