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6 Function Diversity Within Folds and Superfamilies 159Fig. 6.3 KEGG cognate ligands identified in PROCOGNATE as binding HUP domains (CATHsuperfamily 3.40.50.620). Three major categories of ligands are distinguished for clarity: (a)adenine-containing ligands and derivatives thereof, (b) amino-acids and derivatives thereof, and(c) diverse ligands that cannot be classified in either of the above two categories. Many moremolecules (92) are found to bind HUP domains in the PDB but are not shown here. This figureshows that evolutionarily related domains are able to bind to a diverse range of molecules
160 B.H. Dessailly and C.A. OrengoA known example of functional changes between homologous enzymes that isrelated to changes in the environment is described in the literature for the “Twodinucleotide binding domains” flavoproteins, where diversification of functionacross the superfamily has resulted from the conscription of different proteinpartners acting as electron acceptors, via a conserved mode of protein-proteininteractions (Ojha et al. 2007).Enzyme – Non-enzymeA source of functional diversity in superfamilies that is not often discussed in theliterature is that arising from the loss/gain of catalytic capability between homologues.Indeed, the analysis of non-enzymatic proteins is not as straightforwardas that of enzymes, for which several annotation systems and analysis tools arenow well-established (e.g. EC, KEGG and CSA; see Section 6.1) (Table 6.1).Non-enzymatic proteins are nevertheless frequently found in so-called enzymaticfamilies. The processes by which a protein loses catalytic capabilities are fairlystraightforward as the mere loss of a single crucial catalytic residue by substitutionwill generally lead to a loss of the enzymatic activity (Todd et al. 2002). The superfamilyof HUP domains (CATH code 3.40.50.620) consists mostly of enzymes, butcontains a few isolated examples of proteins with no known catalytic activity. Forexample, subunits of electron transferring flavoproteins constitute a separate functionalfamily and display significant sequence, structure, and function alterationsfrom other members of the superfamily (Aravind et al. 2002). An example at anotherlevel within that superfamily is that of the cryptochrome DASH, a non-enzyme thatshows striking similarities with evolutionarily related DNA repair photolyases interms of DNA binding and redox-dependent function, but also major differencesnotably in the active site (Brudler et al. 2003). There are also examples of superfamiliesthat are largely dominated by non-enzymes, such as the Periplasmic-Binding-Protein like domains (CATH code 3.40.190.10) in which many distinctfunctional families are identified on the basis of the molecules to which they bind,or of their role in the context of the cell, e.g. transporters or surface receptors.Extreme Examples of Functionally Diverse SuperfamiliesFrom the above discussion on mechanistically diverse and specificity diversesuperfamilies, it appears that most superfamilies maintain some degree of functionalcommonality between members in spite of their divergence. This is to beexpected since superfamilies consist of evolutionarily related proteins by definition,and the rules of parsimony make it reasonable to assume that homologousproteins may retain at least some aspect of their function in the course of evolution.However, examples of superfamilies also exist in which such commonalitieshave not been uncovered yet. In the previously mentioned analysis of large anddiverse superfamilies by Todd et al., one superfamily – the Hexapeptide RepeatProteins – displayed neither commonalities in catalytic mechanism nor in substrateselectivity (Todd et al. 2001). Another example of superfamily for which anyfunctional similarity fails to emerge between members is that of the HUP-domains.
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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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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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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
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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
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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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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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320 IndexConsensus templates, 205Co
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322 IndexLigand-binding templates,
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324 IndexStructure-function linkage
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