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6 Function Diversity Within Folds and Superfamilies 145Table 6.1 URLs and short descriptions of databases and tools of interest mentioned in thetextName URL DescriptionCATH http://cathwww.biochem.ucl.ac.uk/ Structural classification of proteinsSCOPhttp://scop.mrc-lmb.cam.ac.uk/ Structural classification of proteinsscop/SFLD http://sfld.rbvi.ucsf.edu/ Functional classification of enzymesuperfamiliesPROCOGNATE http://www.ebi.ac.uk/thornton-srv/databases/procognate/index.Mapping of domains to their cognateligandshtmlGene Ontology http://www.geneontology.org Controlled vocabulary of proteinfunctionsEChttp://www.chem.qmul.ac.uk/ Classification of enzymatic reactionsiubmb/enzyme/EzCatDB http://mbs.cbrc.jp/EzCatDB/ Database of enzyme catalytic mechanismsMACiEhttp://www.ebi.ac.uk/thornton-srv/databases/MACiE/Database of enzyme reaction mechanismsKEGG http://www.genome.jp/kegg/ Integrated representation of genes,gene products and pathwaysFUNCAT http://mips.gsf.de/projects/funcat Annotation scheme of protein functionsDALIhttp://ekhidna.biocenter.helsinki. Structure alignmentfi/dali_serverFATCAT http://fatcat.burnham.org/ Flexible structure alignmentSSM http://www.ebi.ac.uk/msd-srv/ssm/ Structure alignment by secondarystructure matchingCATHEDRALhttp://www.cathdb.info/cgi-bin/CathedralServer.plAlgorithm to detect known folds inprotein structures(Ruepp et al. 2004). KEGG and FUNCAT have traditionally been more focused onthe biological processes in which proteins are involved rather than their molecularactivities, but both of these databases can nevertheless provide very useful cluesregarding what is referred to as molecular function in GO.6.2 From Fold to Function6.2.1 Definition of a Fold6.2.1.1 General UnderstandingThe fold adopted by a protein is generally understood as the global arrangementof its main elements of secondary structures, both in terms of their relative orientationsand of their topological connections. A major difficulty directly arises from
146 B.H. Dessailly and C.A. Orengothis general statement since there are no objective rules to decide which are themain elements of secondary structure to be considered for defining the fold(Grishin 2001).One objective of this chapter is to describe how knowledge of relationshipsbetween proteins, such as sharing the same fold, helps in transferring functionalannotations from well-characterised proteins to proteins of unknown function. Aswill be discussed further in Section 6.3, the main assumption made in the processof transferring annotations between proteins is that evolutionarily related (i.e.homologous) proteins generally tend to share functional properties. But proteinsadopting the same fold are not necessarily homologous. It has been argued thatproteins can attain a given fold independently by convergent evolution, becauseonly a limited number of folds are physically acceptable (Russell et al. 1997). Forexample, it is not clear whether all superfamilies of proteins that adopt the TIM-like(β/α) 8barrel fold are evolutionarily related, as definitive evidence in that sense hasnot been found (Nagano et al. 2002).6.2.1.2 Practical DefinitionsSeveral databases have been set-up to classify protein structures into a comprehensiveframework of structural relationships. The practical definition of a foldused in the most-widely used of these databases is given below. As will emergefrom the following definitions, the concept of fold is generally applied todomains rather than full-length proteins, but the definition of a domain can varybetween databases.CATH – The CATH database is a hierarchical classification of protein domainstructures (Orengo et al. 1997; Greene et al. 2007). The highest level of classificationassigns protein domains to three different classes based on their global contentin secondary structures. Within CATH classes, protein domains are classified intodifferent architectures that describe the orientation of secondary structures withoutconsidering their connectivity. Domains in a given architecture are further sub-classifiedinto different topologies, depending on how secondary structures are connectedto one another. It is this topology level that fits most closely to the generalnotion of a fold described above. In practise, assignment of domains to the topologiesin CATH is performed automatically with the structural alignment programSSAP (Orengo and Taylor 1996) and empirically derived cut-offs.SCOP – Like CATH, the Structural Classification of Proteins (SCOP) is ahierarchical classification of protein domain structures (Murzin et al. 1995;Andreeva et al. 2008), but the levels of classification differ between the two databases.As in CATH, the highest level of classification in SCOP is the structuralclass, but SCOP defines four different classes whereas CATH defines three. Thenext level of classification is the fold; two protein domains are assigned to thesame fold if they share the same major elements of secondary structure arrangedin a similar orientation, and with the same topological connections. This definitioncorresponds well to the definition of the topology level in CATH but, in practise,
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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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2 Fold Recognition 37dependent on t
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2 Fold Recognition 39based on the r
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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 452.3.4 Consensu
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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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2 Fold Recognition 55Tress ML, Jone
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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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64 A. Fiserbetween fold recognition
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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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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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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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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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