From Protein Structure to Function with Bioinformatics.pdf

More documents

Recommendations

Info

6 Function Diversity Within Folds and Superfamilies 163ReferencesAdams MA, Suits MD, Zheng J, et al. (2007) Piecing together the structure-function puzzle:experiences in structure-based functional annotation of hypothetical proteins. Proteomics7:2920–2932Andreeva A, Murzin AG (2006) Evolution of protein fold in the presence of functional constraints.Curr Opin Struct Biol 16:399–408Andreeva A, Howorth D, Chandonia JM, et al. (2008) Data growth and its impact on the SCOPdatabase: new developments. Nucleic Acids Res 36:D419–D425Aravind L, Anantharaman V, Koonin EV (2002) Monophyly of class I aminoacyl tRNA synthetase,USPA, ETFP, photolyase, and PP-ATPase nucleotide-binding domains: implicationsfor protein evolution in the RNA. Proteins 48:1–14Bashton M, Chothia C (2007) The generation of new protein functions by the combination ofdomains. Structure 15:85–99Bashton M, Nobeli I, Thornton JM (2006) Cognate ligand domain mapping for enzymes. J MolBiol 364:836–852Bashton M, Nobeli I, Thornton JM (2008) PROCOGNATE: a cognate ligand domain mapping forenzymes. Nucleic Acids Res 36:D618–D622Brudler R, Hitomi K, Daiyasu H, et al. (2003) Identification of a new cryptochrome class.Structure, function, and evolution. Mol Cell 11:59–67Burroughs AM, Allen KN, Dunaway-Mariano D, et al. (2006) Evolutionary genomics of the HADsuperfamily: understanding the structural adaptations and catalytic diversity in a superfamilyof phosphoesterases and allied enzymes. J Mol Biol 361:1003–1034Colovos C, Cascio D, Yeates TO (1998) The 1.8 A crystal structure of the ycaC gene product fromEscherichia coli reveals an octameric hydrolase of unknown specificity. Structure6:1329–1337Dessailly BH, Lensink MF, Orengo CA, et al. (2008) LigASite–a database of biologically relevantbinding sites in proteins with known apo-structures. Nucleic Acids Res 36:D667–D673Devos D, Valencia A (2000) Practical limits of function prediction. Proteins 41:98–107Devos D, Valencia A (2001) Intrinsic errors in genome annotation. Trends Genet 17:429–431Dolinski K, Botstein D (2007) Orthology and functional conservation in eukaryotes. Annu RevGenet 41:465–507Favia AD, Nobeli I, Glaser F, et al. (2008) Molecular docking for substrate identification: theshort-chain dehydrogenases/reductases. J Mol Biol 375:855–874Furukawa H, Singh SK, Mancusso R, et al. (2005) Subunit arrangement and function in NMDAreceptors. Nature 438:185–192Gerlt JA, Babbitt PC (2001) Divergent evolution of enzymatic function: mechanisticallydiverse superfamilies and functionally distinct suprafamilies. Annu Rev Biochem70:209–246Glasner ME, Gerlt JA, Babbitt PC (2006) Evolution of enzyme superfamilies. Curr Opin ChemBiol 10:492–497Goldstein RA (2008) The structure of protein evolution and the evolution of protein structure.Curr Opin Struct Biol 18:170–177Greene LH, Lewis TE, Addou S, et al. (2007) The CATH domain structure database: new protocolsand classification levels give a more comprehensive resource for exploring evolution.Nucleic Acids Res 35:D291–D297Grishin NV (2001) Fold change in evolution of protein structures. J Struct Biol 134:167–185Harrison A, Pearl F, Mott R, et al. (2002) Quantifying the similarities within fold space. J MolBiol 323:909–926Harrison PM, Gerstein M (2002) Studying genomes through the aeons: protein families, pseudogenesand proteome evolution. J Mol Biol 318:1155–1174Hegyi H, Gerstein M (2001) Annotation transfer for genomics: measuring functional divergencein multi-domain proteins. Genome Res 11:1632–1640
164 B.H. Dessailly and C.A. OrengoHolliday GL, Almonacid DE, Bartlett GJ, et al. (2007) MACiE (Mechanism, Annotation andClassification in Enzymes): novel tools for searching catalytic mechanisms. Nucleic Acids Res35:D515–D520Holm L, Sander C (1993) Protein structure comparison by alignment of distance matrices. J MolBiol 233:123–138Holm L, Sander C (1996a) Mapping the protein universe. Science 273:595–603Holm L, Sander C (1996b) The FSSP database: fold classification based on structure-structurealignment of proteins. Nucleic Acids Res 24:206–209Horowitz NH (1945) On the evolution of biochemical syntheses. Proc Natl Acad Sci USA31:153–157Jeffery CJ (2003) Moonlighting proteins: old proteins learning new tricks. Trends Genet19:415–417Jiang H, Blouin C (2007) Insertions and the emergence of novel protein structure: a structurebasedphylogenetic study of insertions. BMC Bioinformatics 8:444Kanehisa M, Araki M, Goto S, et al. (2008) KEGG for linking genomes to life and the environment.Nucleic Acids Res 36:D480–D484Khersonsky O, Roodveldt C, Tawfik DS (2006) Enzyme promiscuity: evolutionary and mechanisticaspects. Curr Opin Chem Biol 10:498–508Kolodny R, Koehl P, Levitt M (2005) Comprehensive evaluation of protein structure alignmentmethods: scoring by geometric measures. J Mol Biol 346:1173–1188Kolodny R, Petrey D, Honig B (2006) Protein structure comparison: implications for the nature of‘fold space’, and structure and function prediction. Curr Opin Struct Biol 16:393–398Kraulis PJ (1991) Molscript: a program to produce both detailed and schematic plots of proteinstructures. J Appl Cryst 24:946–950Krissinel E, Henrick K (2004) Secondary-structure matching (SSM), a new tool for fast proteinstructure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr 60:2256–2268Lee D, Grant A, Marsden RL, et al. (2005) Identification and distribution of protein families in120 completed genomes using Gene3D. Proteins 59:603–615Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. NatRev Mol Cell Biol 8:995–1005Lopez G, Valencia A, Tress M (2007) FireDB–a database of functionally important residues fromproteins of known structure. Nucleic Acids Res 35:D219–D223Marsden RL, Ranea JA, Sillero A, et al. (2006) Exploiting protein structure data to explore theevolution of protein function and biological complexity. Philos Trans R Soc Lond B Biol Sci361:425–440Martin AC, Orengo CA, Hutchinson EG, et al. (1998) Protein folds and functions. Structure6:875–884Merritt EA, Bacon DJ (1997) Raster3d version 2: photorealistic molecular graphics. MethodEnzymol 277:505–524Moult J, Melamud E (2000) From fold to function. Curr Opin Struct Biol 10:384–389Murzin AG, Brenner SE, Hubbard T, et al. (1995) SCOP: a structural classification of proteinsdatabase for the investigation of sequences and structures. J Mol Biol 247:536–540Nagano N (2005) EzCatDB: the Enzyme Catalytic-mechanism Database. Nucleic Acids Res 33:D407–D412Nagano N, Orengo CA, Thornton JM (2002) One fold with many functions: the evolutionaryrelationships between TIM barrel families based on their sequences, structures and functions.J Mol Biol 321:741–765Nomenclature Committee of the International Union of Biochemistry and Molecular Biology andWebb EC (1992) Enzyme Nomenclature: Recommendations of the Nomenclature Committeeof the International Union of Biochemistry and Molecular Biology on the Nomenclature andClassification of Enzymes. Academic, San Diego, CAO’Boyle NM, Holliday GL, Almonacid DE, et al. (2007) Using reaction mechanism to measureenzyme similarity. J Mol Biol 368:1484–1499
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 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
Page 176 and 177: 7 Predicting Protein Function from
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
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:
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
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 278 and 279:
272 R.A. LaskowskiXenarios I, Salwi
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 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
Page 316 and 317:
Page 318 and 319:
Page 320 and 321:
Page 322 and 323:
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

Create successful ePaper yourself

Delete template?

Save as template?