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8 3D Motifs 193structures together. A vertex in the graph represents a pair of atoms or pseudoatoms,one from structure A and one from structure B (where “structure” could be a 3Dmotif). Only atoms with matching types are allowed to pair. Two vertices are connectedby an edge if the distance between the two atoms in A matches the distancebetween the two atoms in B, within the specified tolerance. A clique is a graph inwhich every vertex is connected to every other vertex. Thus, clique detection identifiesa set of atoms from A with internal distances completely consistent withthose among a paired set of atoms from B.● Depth-first searching. Structures are searched exhaustively for the presence of a3D motif. The search space is limited by restrictions on the atom or residue typesallowed to match, and by geometric cutoffs such as distance tolerances and anupper bound on the overall root-mean-square deviation (RMSD).● Match extension. Partial or seed matches to a 3D motif are initially identified,and then attempts are made to extend the match to the entire motif.These methods will all seek motifs in a given static structure. Recent data showthat combining a motif searching algorithm with Molecular Dynamics simulationsthat sample conformational space (see also Chapter 9) is a promising, albeit computationallyexpensive, avenue towards improving the results of the search (Glazeret al. 2008).8.2.3 Interpretation of ResultsMany web servers are available for comparing query structures to databases of 3Dmotifs (Table 8.1). What can be inferred about the function of a query when itmatches a motif? Several issues must be considered when deciding whether amatch is meaningful, and if so, what it means.What function is implied depends on how the motif was generated. If the motifrepresents a set of residues experimentally determined to be important for a particularfunction, a match suggests the query structure may perform that function.Similarly, matches to a motif based on multiple structures that perform some function(positive examples) are suggestive of that function, especially if the motif wasdesigned to exclude negative examples. What function is implied, however, dependson the criteria for membership in the positive set. For example, if the structures inthe positive set all bind adenosine but catalyze a variety of reactions, a match wouldimply adenosine binding but not necessarily adenosine deaminase activity. If thepositive set consists of representatives from a SCOP superfamily, a match wouldsuggest that the query protein also belongs to that superfamily, but would not implyanything more specific about its function.Ideally, for motif discovery the set of positive examples should be as diverseas possible while retaining the common feature, and the negative examplesshould be as similar as possible to the positive examples while lacking that feature.In practice, the positive and negative sets may not be ideal, and part or all of
194 E.C. Meng et al.Table 8.1 Web servers for 3D motif searching and comparisonName and URL Server function 3D motif database aCatalytic Site Atlas (CSA)www.ebi.ac.uk/thorntonsrv/Databases/cgi-bin/CSS/makeEbiHtml.cgi?file=form.htmlfunClustpdbfun.uniroma2.it/FunclustGASPSdbgaspsdb.rbvi.ucsf.eduPAR-3Dsunserver.cdfd.org.in:8080/protease/PAR_3DpdbFunpdbfun.uniroma2.itPDBSiteScanwww.mgs.bionet.nsc.ru/cgi-bin/mgs/fastprot/pdbsitescan.pl?stage=0PINTSwww.russell.embl.de/pintsWeekly results: www.russell.embl.de/pints-weeklyCompares query to motif databasewith JESS, estimatessignificanceIdentifies motifs shared bythree or more of up to20 input structures usingQuery3DCompares query to 3D motifsrepresenting SCOP andGO classifications usingRIGOR b , estimatessignificanceCompares query to motifsexpressed as distance andangle rangesCompares specified probeand target residue setsusing Query3DCompares query to all or asubset of motifs in thePDBSite databaseCompares query to motif database,user-defined motifto protein database, or twoproteins to each other;estimates significanceAlpha-carbon/beta-carbon andfunctional atom motifsfor 147 well-characterizedenzyme families, also available for download:www.ebi.ac.uk/thornton-srv/databases/Cgi-bin/CSS/makeEbiHtml.cgi?File=downloadTemplateLibrary.html(No database)Alpha-carbon/side chain centroidmotifs: 4,385 motifsrepresenting 272 GOmolecular functions, 3,599motifs representing 186SCOP superfamilies and137 SCOP families, 4,581motifs representing 376groups in which proteinsshare both SCOP superfamilyand GO molecularfunctionAlpha-carbon/beta-carbonmotifs for six proteaseclasses and ten glycolyticpathway enzymes, alphacarbon/beta-carbon/sidechain pseudoatom motifsfor metal-binding sites ofthree or four residues>12 million individual residuesof which sets can be specifiedwith Boolean combinationsof descriptors36,273 backbone-atom motifsbased on SITE annotationsof individual structures ortheir interfaces with otherproteins, RNA, and DNALigand-binding and SITEannotatedmotifs consistingof side chain points frompolar residues(continued)
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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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9 Protein Dynamics: From Structure
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252 R.A. LaskowskiConsequently, man
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254 R.A. Laskowskiucla.edu, and Pro
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256 R.A. LaskowskiFig. 10.2 Gene On
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258 R.A. Laskowski10.2.5 Protein In
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260 R.A. LaskowskiFig. 10.4 Schemat
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262 R.A. Laskowskiaccessibility and
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264 R.A. LaskowskiFig. 10.6 A ligan
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266 R.A. LaskowskiGly97Gly99Tyr98Ty
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268 R.A. LaskowskiFig. 10.8 Example
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270 R.A. LaskowskiReferencesAltschu
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272 R.A. LaskowskiXenarios I, Salwi
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274 J.D. Watson and J.M. ThorntonTh
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276 J.D. Watson and J.M. ThorntonTa
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282 J.D. Watson and J.M. Thorntonin
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288 J.D. Watson and J.M. ThorntonPD
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290 J.D. Watson and J.M. ThorntonKi
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Chapter 12Prediction of Protein Fun
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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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