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Table 7.1 Online resources and tools relevant to function prediction by analysis of proteinsurfacesMethodURLFunction prediction using protein surfaceef-Sitehttp://ef-site.hgc.jpPatchFinderPlushttp://pfp.technion.ac.il/Scorconshttp://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/valdar/scorecons_server.plConSurfhttp://consurf.tau.ac.il/hotpatchhttp://hotpatch.mbi.ucla.eduLigand binding site predictionPASShttp://www.ccl.net/cca/software/UNIX/pass/overview.shtmlCASTphttp://sts-fw.bioengr.uic.edu/castp/SurfNethttp://www.biochem.ucl.ac.uk/~roman/surfnet/surfnet.htmlPDBsumhttp://www.ebi.ac.uk/pdbsum/Pocket-Finderhttp://www.bioinformatics.leeds.ac.uk/pocketfinderLIGSITE cschttp://scoppi.biotec.tu-dresden.de/pocket/PocketPickerhttp://gecco.org.chemie.uni-frankfurt.de/pocketpicker/index.htmlQ-SiteFinderhttp://www.bioinformatics.leeds.ac.uk/qsitefinderTHEMATICShttp://pfweb.chem.neu.edu/thematics/submit.htmlProtein-protein interface predictionSharp 2http://www.bioinformatics.sussex.ac.uk/SHARP2/sharp2.htmlPPI-PREDhttp://www.bioinformatics.leeds.ac.uk/ppi_pred/InterProSurfhttp://curie.utmb.edu/Cons-PPISPhttp://pipe.scs.fsu.edu/ppisp.htmlProMatehttp://bioportal.weizmann.ac.il/promate/Fig. 7.7 Interface prediction with PPI-Pred. Showing the predicted binding sites on the surface offerredoxin reductase (PDB code: 1EWY, chain A). Label A indicates the position of the top rankedbinding site patch, which corresponds closely with the true ferredoxin binding site, B and C are thesecond and third ranked patches respectively covering other areas on the protein surface
184 N.J. Burgoyne and R.M. Jacksonchemical character propensities, residue neighbour propensity, residue conservation,secondary structure, residue sequence separation, the length of loops and the locationof crystallographic water to similar effect. Several other related approaches arealso listed in Table 7.1. A comparison of the performance of these methods hasbeen made (Zhou and Qin 2007).7.6 SummaryThis chapter has detailed the current approaches which use surface properties in anumber of important applications including: protein function prediction; predictingbinding site location and druggability; annotation of ligand binding sites for structure-baseddrug design; and protein interface prediction. The various online toolsand methods have been introduced and the interested reader is referred to the publishedarticles for further details. We have attempted to give a broad overview of themethods and their practical applications; however, this is not a comprehensivereview of all methods in this important and emerging area.ReferencesAntosiewicz J, McCammon JA, Gilson MK (1994) Prediction of pH-dependent properties ofproteins. J Mol Biol 238:415–436.Beadle BM, Shoichet BK (2002) Structural bases of stability-function tradeoffs in enzymes. J MolBiol 321:285–296.Berchanski A, Shapira B, Eisenstein M (2004) Hydrophobic complementarity in protein-proteindocking. Proteins 56:130–142.Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography ofproteins. Nucleic Acids Res 31:3352–3355.Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol280:1–9.Bordner AJ, Abagyan R (2005) Statistical analysis and prediction of protein-protein interfaces.Proteins 60:353–366.Bradford JR, Westhead DR (2005) Improved prediction of protein-protein binding sites using asupport vector machines approach. Bioinformatics 21:1487–1494.Brady GP Jr, Stouten PF (2000) Fast prediction and visualization of protein binding pockets withPASS. J Comput Aided Mol Des 14:383–401.Brown D, Superti-Furga G (2003) Rediscovering the sweet spot in drug discovery. Drug DiscovToday 8:1067–1077.Burgoyne NJ, Jackson RM (2006) Predicting protein interaction sites: binding hot-spots in protein-proteinand protein-ligand interfaces. Bioinformatics 22:1335–1342.Cheng AC, Coleman RG, Smyth KT, et al. (2007) Structure-based maximal affinity model predictssmall-molecule druggability. Nat Biotechnol 25:71–75.Chothia C., Janin J (1975) Principles of protein-protein recognition. Nature 256: 705–708.Clackson T, Wells JA (1995) A hot spot of binding energy in a hormone-receptor interface.Science 267:383–386.Connolly ML (1984) Analytical molecular surface calculation. J Appl Cryst 16:548–558.
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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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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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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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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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Page 196 and 197: 8 3D Motifs 189clustering similar s
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Page 202 and 203: 8 3D Motifs 195Table 8.1 (continued
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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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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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