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88 A. FiserPetrey D, Xiang Z, Tang CL, et al. (2003) Using multiple structure alignments, fast model building,and energetic analysis in fold recognition and homology modeling. Proteins 53(Suppl6):430–435Pieper U, Eswar N, Davis FP, et al. (2006) MODBASE: a database of annotated comparative proteinstructure models and associated resources. Nucleic Acids Res 34:D291–295Pillardy J, Czaplewski C, Liwo A, et al. (2001) Recent improvements in prediction of proteinstructure by global optimization of a potential energy function. Proc Natl Acad Sci USA98:2329–23233Qian B, Ortiz AR, Baker D (2004) Improvement of comparative model accuracy by free-energyoptimization along principal components of natural structural variation. Proc Natl Acad SciUSA 101:15346–15351Rai BK, Fiser A (2006) Multiple mapping method: a novel approach to the sequence-to-structurealignment problem in comparative protein structure modeling. Proteins 63:644–661Rai BK, Madrid-Aliste CJ, Fajardo JE, et al. (2006) MMM: a sequence-to-structure alignmentprotocol. Bioinformatics 22:2691–2692Reddy BV, Li WW, Shindyalov IN, et al. (2001) Conserved key amino acid positions (CKAAPs)derived from the analysis of common substructures in proteins. Proteins 42:148–163Ring CS, Cohen FE (1993) Modeling protein structures: construction and their applications.FASEB J 7:783–890Ring CS, Sun E, McKerrow JH, et al. (1993) Structure-based inhibitor design by using proteinmodels for the development of antiparasitic agents. Proc Natl Acad Sci USA 90:3583–3587Rohl CA, Strauss CE, Chivian D, et al. (2004) Modeling structurally variable regions in homologousproteins with rosetta. Proteins 55:656–677.Rost B (1997) Protein structures sustain evolutionary drift. Fold Des 2:S19–S24Rost B (1999) Twilight zone of protein sequence alignments. Protein Eng 12:85–94Rudolph MG, Stanfield RL, Wilson IA (2006) How TCRs bind MHCs, peptides, and coreceptors.Annu Rev Immunol 24:419–466Rusch DB, Halpern AL, Sutton G, et al. (2007) The Sorcerer II Global Ocean Sampling expedition:northwest Atlantic through eastern tropical Pacific. PLoS Biol 5:e77Rychlewski L, Jaroszewski L, Li W, et al. (2000) Comparison of sequence profiles. Strategies forstructural predictions using sequence information. Protein Sci 9:232–241Rykunov D, Fiser A (2007) Effects of amino acid composition, finite size of proteins, and sparsestatistics on distance-dependent statistical pair potentials. Proteins 67:559–568Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints.J Mol Biol 234:779–815Sali A, Matsumoto R, McNeil HP, et al. (1993) Three-dimensional models of four mouse mast cellchymases. Identification of proteoglycan binding regions and protease-specific antigenicepitopes. J Biol Chem 268:9023–9034Sali A, Shakhnovich E, Karplus M (1994) How does a protein fold? Nature 369:248–251Samudrala R, Moult J (1998) A graph-theoretic algorithm for comparative modeling of proteinstructure. J Mol Biol 279:287–302Sanchez R, Sali A (1997) Evaluation of comparative protein structure modeling by MODELLER-3. Proteins(Suppl 1):50–58Sanchez R, Sali A (1998) Large-scale protein structure modeling of the Saccharomyces cerevisiaegenome. Proc Natl Acad Sci USA 95:13597–13602Sangar V, Blankenberg DJ, Altman N, et al. (2007) Quantitative sequence-function relationshipsin proteins based on gene ontology. BMC Bioinformatics 8:294Saraste M, Sibbald PR, Wittinghofer A (1990) The P-loop–a common motif in ATP- and GTPbindingproteins. Trends Biochem Sci 15:430–434Sauder JM, Arthur JW, Dunbrack RL, Jr. (2000) Large-scale comparison of protein sequencealignment algorithms with structure alignments. Proteins 40:6–22Schaffer AA, Aravind L, Madden TL, et al. (2001) Improving the accuracy of PSI-BLAST proteindatabase searches with composition-based statistics and other refinements. Nucleic Acids Res29:2994–3005
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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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Page 50 and 51: 2 Fold Recognition 39based on the r
Page 52 and 53: 2 Fold Recognition 41The idea of co
Page 54 and 55: 2 Fold Recognition 43query sequence
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Page 58 and 59: 2 Fold Recognition 472.4 Alignment
Page 60 and 61: 2 Fold Recognition 49statistical me
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Page 64 and 65: 2 Fold Recognition 53Berman HM, Wes
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Page 68 and 69: 58 A. Fiserwith more than 50% seque
Page 70 and 71: 60 A. FiserIn contrast to ab initio
Page 72 and 73: 62 A. FiserTable 3.1 (continued)SWI
Page 74 and 75: 64 A. Fiserbetween fold recognition
Page 76 and 77: 66 A. FiserFig. 3.1 Comparing accur
Page 78 and 79: 68 A. Fiserinto conserved core regi
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Page 92 and 93: 82 A. FiserImproved and new methods
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Page 122 and 123: Chapter 5Bioinformatics Approaches
Page 124 and 125: 5 Structure and Function of Intrins
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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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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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