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6 Function Diversity Within Folds and Superfamilies 165Ojha S, Meng EC, Babbitt PC (2007) Evolution of function in the “Two Dinucleotide BindingDomains” flavoproteins. PLoS Comput Biol 3(7):e121Orengo CA (1999) CORA–topological fingerprints for protein structural families. Protein Sci8:699–715Orengo CA, Taylor WR (1996) SSAP: sequential structure alignment program for protein structurecomparison. Method Enzymol 266:617–635Orengo CA, Jones DT, Thornton JM (1994) Protein superfamilies and domain superfolds. Nature372:631–634Orengo CA, Michie AD, Jones S, et al. (1997) CATH–a hierarchic classification of protein domainstructures. Structure 5:1093–1108Pegg SC, Brown SD, Ojha S, et al. (2006) Leveraging enzyme structure-function relationships forfunctional inference and experimental design: the structure-function linkage database.Biochemistry 45:2545–2555Piatigorsky J, Kantorow M, Gopal-Srivastava R, et al. (1994) Recruitment of enzymes and stressproteins as lens crystallins. EXS 71:241–250Porter CT, Bartlett GJ, Thornton JM (2004) The Catalytic Site Atlas: a resource of catalytic sitesand residues identified in enzymes using structural data. Nucleic Acids Res 32:D129–D133Redfern OC, Harrison A, Dallman T, et al. (2007) CATHEDRAL: a fast and effective algorithmto predict folds and domain boundaries from multidomain protein structures. PLoS ComputBiol 3(11):e232Reeves GA, Dallman TJ, Redfern OC, et al. (2006) Structural diversity of domain superfamiliesin the CATH database. J Mol Biol 360:725–741Reid AJ, Yeats C, Orengo CA (2007) Methods of remote homology detection can be combined toincrease coverage by 10% in the midnight zone. Bioinformatics 23:2353–2360Rison SC, Thornton JM (2002) Pathway evolution, structurally speaking. Curr Opin Struct Biol12:374–382Rost B (2002) Enzyme function less conserved than anticipated. J Mol Biol 318:595–608Ruepp A, Zollner A, Maier D, et al. (2004) The FunCat, a functional annotation scheme for systematicclassification of proteins from whole genomes. Nucleic Acids Res 32:5539–5545Russell RB, Saqi MA, Sayle RA, et al. (1997) Recognition of analogous and homologous proteinfolds: analysis of sequence and structure conservation. J Mol Biol 269:423–439Russell RB, Sasieni PD, Sternberg MJ (1998) Supersites within superfolds. Binding site similarityin the absence of homology. J Mol Biol 282:903–918Sangar V, Blankenberg DJ, Altman N, et al. (2007) Quantitative sequence-function relationshipsin proteins based on gene ontology. BMC Bioinformatics 8:294Shakhnovich BE, Koonin EV (2006) Origins and impact of constraints in evolution of genefamilies. Genome Res 16:1529–1536Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorialextension (CE) of the optimal path. Protein Eng 11:739–747Takahashi H, Inagaki E, Kuroishi C, et al. (2004) Structure of the Thermus thermophilus putative periplasmicglutamate/glutamine-binding protein. Acta Crystallogr D Biol Crystallogr. 60:1846–1854Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein families. Science278:631–637The Gene Ontology Consortium (2000) Gene ontology: tool for the unification of biology. NatGenet 25:25–29Tian W, Skolnick J (2003) How well is enzyme function conserved as a function of pairwisesequence identity? J Mol Biol 333:863–882Todd AE, Orengo CA, Thornton JM (2001) Evolution of function in protein superfamilies, froma structural perspective. J Mol Biol 307:1113–1143Todd AE, Orengo CA, Thornton JM (2002) Sequence and structural differences between enzymeand nonenzyme homologs. Structure 10:1435–1451Whisstock JC, Lesk AM (2003) Prediction of protein function from protein sequence and structure.Q Rev Biophys 36:307–340
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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
Page 122 and 123: Chapter 5Bioinformatics Approaches
Page 124 and 125: 5 Structure and Function of Intrins
Page 150 and 151: Chapter 6Function Diversity Within
Page 152 and 153: 6 Function Diversity Within Folds a
Page 174 and 175: Chapter 7Predicting Protein Functio
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Page 190 and 191: Table 7.1 Online resources and tool
Page 194 and 195: Chapter 83D MotifsElaine C. Meng, B
Page 196 and 197: 8 3D Motifs 189clustering similar s
Page 198 and 199: 8 3D Motifs 191To improve the signa
Page 200 and 201: 8 3D Motifs 193structures together.
Page 202 and 203: 8 3D Motifs 195Table 8.1 (continued
Page 204 and 205: 8 3D Motifs 1978.3.1 User-Defined M
Page 206 and 207: 8 3D Motifs 199Fig. 8.3 The FFF mot
Page 208 and 209: 8 3D Motifs 2018.3.2 Motif Discover
Page 210 and 211: 8 3D Motifs 203In addition to SITE
Page 212 and 213: 8 3D Motifs 205folds; they shared a
Page 214 and 215: 8 3D Motifs 207from a training set
Page 216 and 217: 8 3D Motifs 209Table 8.3 Web server
Page 218 and 219: 8 3D Motifs 211its greater overall
Page 220 and 221: 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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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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278 J.D. Watson and J.M. Thorntonva
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280 J.D. Watson and J.M. Thorntonfo
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282 J.D. Watson and J.M. Thorntonin
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284 J.D. Watson and J.M. Thorntonan
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286 J.D. Watson and J.M. ThorntonFi
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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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