324 Index<strong>Structure</strong>-function linkage database (SFLD),145, 150, 152, 155–157Sharp2, 182, 183Short Linear Motif Discovery (SliMDisc), 129Short linear motif (SLiM), 97, 127, 129Shwachman-Bodian-Diamond syndrome,285, 286Signal peptide, 99–101, 105, 129Signal transduction, 92, 113, 114, 123, 128,144, 218Simulated annealing, 14, 37, 69, 73, 107Simulated tempering, 228Simulation methodology, 218Single-point-centred descriptions, 208Singular-value decomposition, 230SOIPPA, 188, 205Solvation potential, 17, 18, 32, 73Solvent accessible surface, 168–170SPASM, 188, 199, 200, 203, 207Specificity diverse superfamilies, 157, 160SpoVS, 308Statistical significance, 47–49, 187, 196,200–202, 205, 206, 208, 209Structural embellishments, 155, 282Structural genomics (SG), 27, 58, 60, 78, 81,113, 143, 147, 149, 152, 167, 171, 188,189, 209, 211, 251, 254, 259, 273–289,293, 295, 302, 307Structural homology, 151, 162Structural templates, 51, 72, 190, 201, 302<strong>Structure</strong> comparison, 148, 205, 308<strong>Structure</strong> decoys, 5, 12, 17, 19<strong>Structure</strong>-function paradigm, 113, 114<strong>Structure</strong> prediction, 3–20, 28, 30, 32, 39, 41,43, 45, 47, 49, 51, 52, 58, 59, 77, 81,91–108, 116, 122, 126, 230, 295, 303,304, 308, 309<strong>Structure</strong> refinement, 7–9, 15Subfunctionalisation, 153Sub-optimal alignment, 48SuMo, 195, 203Superfamily, 30, 71, 95–97, 143–162, 189, 190,192–195, 199–201, 206–208, 211, 260,281, 282, 303, 307–309, 313Superfamily motifs, 199Superfolds, 149, 150, 152Supersites, 149, 150SuperStar, 179Support vec<strong>to</strong>r machines, 43, 44, 47, 49, 100,102, 103, 117, 120, 128, 178Surface conservation, 171, 173Surface properties, 167–184, 298, 300Surfnet, 175, 183, 262SWISS-MODEL, 62, 81, 106, 297, 298TT4-lysozyme, 222–224TASSER, 6, 7, 12, 17, 19tCONCOORD, 239, 241TEE-REX, 229, 234–237, 243Template-based modelling (TBM), 4, 10, 12,19, 59, 60, 106. See also ComparativemodellingTemplate-free modeling. See Ab initiomodellingTemplates, 4, 8, 10, 12, 18, 19, 29, 30, 35–37,39–43, 45, 47, 48, 50, 51, 58–72,74–76, 78–81, 96, 105, 106, 188, 190,194, 198, 201, 203, 205, 260, 262–267,269, 273, 276, 279, 280, 282, 283, 293,295–302, 304, 306, 308, 309, 312Template search methods, 63, 65Template selection, 64, 69, 78, 106THEMATICS, 177, 183THREADER, 37Threading, 12, 17, 18, 27, 31–38, 41, 42,45, 48–51, 59, 61, 63, 65, 67, 75, 255,305, 308TOPSAN, 287Transcription fac<strong>to</strong>r, 123, 124, 128, 131–134,170, 308Transition pathway, 235–237Transmembrane protein, 93–95, 97, 98Tumor suppressor p53, 114, 119,126, 130UUbiquitination, 124, 125Undirected mining, 190, 201, 202UniProt, 4, 258, 260, 264, 297User-Defined Motifs, 194, 196,197, 202Vvan der Waals surface, 167–169Verify3d, 18, 62, 65, 70, 76WWater permeation, 225Whole genome analysis, 100, 102, 103Wiki, 287XX-ray crystallography, 58, 60, 77, 79, 81, 114,217, 222, 233
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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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18 J. Lee et al.potentials, each re
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22 J. Lee et al.Hsieh MJ, Luo R (20
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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 33distribute the
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2 Fold Recognition 37dependent on t
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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 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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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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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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78 A. FiserA rigorous statistical e
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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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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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7 Predicting Protein Function from
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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 203In addition to SITE
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8 3D Motifs 205folds; they shared a
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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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