From Protein Structure to Function with Bioinformatics.pdf

More documents

Recommendations

Info

272 R.A. LaskowskiXenarios I, Salwinski L, Duan XJ, et al. (2002) DIP, database of interacting proteins: aresearch tool for studying cellular networks of protein interactions. Nucleic Acids Res.30:303–305.Yakunin AF, Yee AA, Savchenko A, et al. (2004) Structural proteomics: a tool for genome annotation.Curr. Opin. Chem. Biol. 8:42–48.
Chapter 11Case Studies: Function Predictionsof Structural Genomics ResultsJames D. Watson and Janet M. ThorntonAbstract The development of high-throughput protein structure determinationpipelines by the various Structural Genomics initiatives around the globe hasresulted in the deposition of several thousand protein structures in the Protein DataBank. However, due to the nature of the target selection process and the requirementfor rapid data release, a significant proportion of these structures have little orno functional information. In order to address this problem a vast array of computationalmethods have been developed to predict a protein’s function from its threedimensional structure. The approaches range from large scale fold comparison tohighly specific residue templates, each with its own advantages and disadvantages.Here we look at the application of these methods in Structural Genomics andreview attempts to determine how successful function prediction from structure hasbeen, with specific examples illustrating some of the success stories.11.1 IntroductionGenome sequencing projects around the globe have already provided enormousamounts of data on the genes that are essential to a number of organisms, and thisinformation is expanding rapidly with the large-scale metagenomics projects currentlyunder way (Yooseph et al. 2007). By comparison, the amount of proteinstructure data available lags far behind. Structural genomics aims to bridge thisgap by solving, in a high-throughput manner, a large number of novel structuresthat can be used to model a larger number of sequences (Fox et al. 2008; Service2005). A consequence of this approach has been the deposition of large numbersof structures with little or no functional annotation. This is in direct contrast totraditional structural biology where the function of a protein is often known inadvance and the structure is often solved to identify the biochemical mechanismsand unique subtleties of its action.J.D. Watson and J.M. Thornton*European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton,Cambridgeshire, CB10 1SD, UK*Corresponding author: e-mail: thornton@ebi.ac.ukD.J. Rigden (ed.) From Protein Structure to Function with Bioinformatics, 273© Springer Science + Business Media B.V. 2009
Page 3:
Daniel John RigdenEditorFrom Protei
Page 8 and 9:
ContentsSection I Generating and In
Page 10 and 11:
Contentsxi4.2.1 Alpha-Helical Bundl
Page 12 and 13:
Contentsxiii7.4.2 Predicting Bindin
Page 14 and 15:
Contentsxv12.3 Accuracy and Added V
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
Page 26 and 27:
14 J. Lee et al.rugged containing m
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 32 and 33:
20 J. Lee et al.viewpoint of struct
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
Page 62 and 63:
2 Fold Recognition 51methods (e.g.
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
Page 76 and 77:
66 A. FiserFig. 3.1 Comparing accur
Page 78 and 79:
68 A. Fiserinto conserved core regi
Page 80 and 81:
70 A. Fiseralignments are built and
Page 82 and 83:
72 A. Fiserfold, such as the hyperv
Page 84 and 85:
74 A. Fiserfunctions delivers impro
Page 86 and 87:
76 A. Fiserfrom efforts of genome s
Page 88 and 89:
78 A. FiserA rigorous statistical e
Page 90 and 91:
80 A. Fiser(Clore et al. 1993). Cha
Page 92 and 93:
82 A. FiserImproved and new methods
Page 94 and 95:
84 A. FiserClaessens M, Van Cutsem
Page 96 and 97:
86 A. FiserHavel TF, Snow ME (1991)
Page 98 and 99:
88 A. FiserPetrey D, Xiang Z, Tang
Page 100 and 101:
90 A. Fiservan Vlijmen HW, Karplus
Page 102 and 103:
92 T. Nugent and D.T. Jones4.2 Stru
Page 104 and 105:
94 T. Nugent and D.T. JonesFig. 4.2
Page 106 and 107:
96 T. Nugent and D.T. JonesTable 4.
Page 108 and 109:
98 T. Nugent and D.T. Jones2000), d
Page 110 and 111:
100 T. Nugent and D.T. JonesTable 4
Page 112 and 113:
102 T. Nugent and D.T. JonesFig. 4.
Page 114 and 115:
104 T. Nugent and D.T. JonesFig. 4.
Page 116 and 117:
106 T. Nugent and D.T. Jonessubdivi
Page 118 and 119:
108 T. Nugent and D.T. Jonescomplex
Page 120 and 121:
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 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Chapter 6Function Diversity Within
Page 152 and 153:
6 Function Diversity Within Folds a
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Chapter 7Predicting Protein Functio
Page 176 and 177:
7 Predicting Protein Function from
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Table 7.1 Online resources and tool
Page 192 and 193:
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
Page 222 and 223:
8 3D Motifs 215Ivanisenko VA, Pintu
Page 224 and 225:
Chapter 9Protein Dynamics: From Str
Page 226 and 227:
9 Protein Dynamics: From Structure
Page 228 and 229: 9 Protein Dynamics: From Structure
Page 258 and 259: 252 R.A. LaskowskiConsequently, man
Page 260 and 261: 254 R.A. Laskowskiucla.edu, and Pro
Page 262 and 263: 256 R.A. LaskowskiFig. 10.2 Gene On
Page 264 and 265: 258 R.A. Laskowski10.2.5 Protein In
Page 266 and 267: 260 R.A. LaskowskiFig. 10.4 Schemat
Page 268 and 269: 262 R.A. Laskowskiaccessibility and
Page 270 and 271: 264 R.A. LaskowskiFig. 10.6 A ligan
Page 272 and 273: 266 R.A. LaskowskiGly97Gly99Tyr98Ty
Page 274 and 275: 268 R.A. LaskowskiFig. 10.8 Example
Page 276 and 277: 270 R.A. LaskowskiReferencesAltschu
Page 280 and 281: 274 J.D. Watson and J.M. ThorntonTh
Page 282 and 283: 276 J.D. Watson and J.M. ThorntonTa
Page 284 and 285: 278 J.D. Watson and J.M. Thorntonva
Page 286 and 287: 280 J.D. Watson and J.M. Thorntonfo
Page 288 and 289: 282 J.D. Watson and J.M. Thorntonin
Page 290 and 291: 284 J.D. Watson and J.M. Thorntonan
Page 292 and 293: 286 J.D. Watson and J.M. ThorntonFi
Page 294 and 295: 288 J.D. Watson and J.M. ThorntonPD
Page 296 and 297: 290 J.D. Watson and J.M. ThorntonKi
Page 298 and 299: Chapter 12Prediction of Protein Fun
Page 300 and 301: 12 Prediction of Protein Function f
Page 324 and 325: 320 IndexConsensus templates, 205Co
Page 326 and 327: 322 IndexLigand-binding templates,
Page 328:
324 IndexStructure-function linkage
show all

From Protein Structure to Function with Bioinformatics.pdf

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?