- Page 3: Daniel John RigdenEditorFrom Protei
- Page 8 and 9: ContentsSection I Generating and In
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- Page 16 and 17: 4 J. Lee et al.about 5.3 million pr
- Page 18 and 19: 6 J. Lee et al.Table 1.1 A list of
- Page 20 and 21: 8 J. Lee et al.2000; Sorin and Pand
- Page 22 and 23: 10 J. Lee et al.Although most knowl
- Page 24 and 25: 12 J. Lee et al.Fig. 1.3 Two exampl
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- Page 28 and 29: 16 J. Lee et al.1.3.4 Mathematical
- Page 30 and 31: 18 J. Lee et al.potentials, each re
- Page 34 and 35: 22 J. Lee et al.Hsieh MJ, Luo R (20
- Page 36 and 37: 24 J. Lee et al.Sippl MJ (1990) Cal
- Page 38 and 39: Chapter 2Fold RecognitionLawrence A
- Page 40 and 41: 2 Fold Recognition 29It has long be
- Page 42 and 43: 2 Fold Recognition 31Fig. 2.2 Graph
- Page 44 and 45: 2 Fold Recognition 33distribute the
- Page 46 and 47: 2 Fold Recognition 35Table 2.1 (a)
- Page 48 and 49: 2 Fold Recognition 37dependent on t
- 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
- Page 56 and 57: 2 Fold Recognition 452.3.4 Consensu
- 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
- Page 66 and 67: 2 Fold Recognition 55Tress ML, Jone
- 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
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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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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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5 Structure and Function of Intrins
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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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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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6 Function Diversity Within Folds a
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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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7 Predicting Protein Function from
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7 Predicting Protein Function from
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7 Predicting Protein Function from
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7 Predicting Protein Function from
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7 Predicting Protein Function from
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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 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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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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9 Protein Dynamics: From Structure
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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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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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12 Prediction of Protein Function f
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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