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8 3D Motifs 209Table 8.3 Web servers for related approachesName and URL Server function DownloadsS-BLESTwww.sblest.orgSiteEnginebioinfo3d.cs.tau.ac.il/SiteEngineWebFEATUREfeature.stanford.edu/webfeatureCompares residue-centred patternsin a query to those ina redundancy-filtered subsetof the PDB, reports the bestmatchingchains and theirannotationsCompares the binding site ofa ligand-bound structure tothe entire surface region ofanother structureCompares query to precalculatedpoint-centred patterns representinga few dozen functionalsitesS-BLEST can also be usedremotely as a web servicevia the visualizationprogram UCSFChimera (www.cgl.ucsf.edu/chimera). DownloadChimera plug-in forWindows, Linux, MacOSX from Life ScienceWeb: www.lifescienceweb.orgLinux executable for noncommercialuse onlyDownload FEATURE codefrom:simtk.org/home/featuresimple biophysical characteristics. The radial distributions of properties aroundpoints representing the function of interest are compared to those around controlpoints, and differences are evaluated for statistical significance. Because values aresummed over spherical shells, however, directional information is lost. TheWebFEATURE server (Liang et al. 2003) (Table 8.3) compares a structure with anyof several precalculated FEATURE patterns representing different types of sites.Over a hundred patterns are available, but the set is rather limited in function space;many of them are simply centred on different atoms in a single type of site.Precomputed matches for a specified pattern, a specified PDB entry, or a set ofstructural genomics proteins are also available.The S-BLEST (Structure-Based Local Environment Search Tool) web server(Mooney et al. 2005; Peters et al. 2006) (Table 8.3) compares a FEATURE patterncentred on each residue in a query structure to a database of such patterns,one for every residue in a nonredundant subset of the PDB. The distribution ofsimilarities for a query residue yields Z-scores for individual database residues.The overall match score of a chain in the database is the average of its K bestresidue Z-scores, where K is a number specified by the user. Chains with scoresbetter than a specified cutoff are listed, along with their GO, EC, and SCOPannotations and similar results from sequence-based comparisons. S-BLEST canalso be used remotely as a web service via the molecular graphics program UCSFChimera (Pettersen et al. 2004). The client software can be downloaded from LifeScience Web (see Table 8.3).
210 E.C. Meng et al.8.5 Docking for Functional AnnotationUltimately, the ligand specificity and catalytic capabilities of a protein depend onthe arrangement of atoms in its binding or active site(s). While 3D motif approachesseek to associate spatial patterns of atoms directly with functions, one can imagineusing the structure of a protein to select likely ligands, then using the resulting predictionsof binding specificity to help infer the protein’s molecular function. Incomputational docking, many small organic molecule structures are each positionedwithin a protein’s binding site and scored by complementarity. Hundreds tothousands of poses may be evaluated for each compound. The top-scoring moleculesfrom a database search are predicted as the most likely to bind the protein andthe best candidates for testing experimentally.This process of computational molecular recognition may sound straightforward,but in practice there are many challenges. One is the large number of metabolitesthat may need to be considered as potential ligands. Another is the difficultyof evaluating structural complementarity rapidly yet accurately enough to distinguishtrue ligands from similar structures that do not bind. For accurate ranking, itmay be necessary to allow conformational flexibility during docking, furtherincreasing the computational burden. Traditionally, database docking has beenapplied to the discovery of drug leads. The need for accurate rankings is evengreater in functional annotation than in lead discovery, because screening experimentallyfor an unknown catalytic activity is much more difficult than screening forbinding, and because the goal is to annotate thousands of proteins in an automatedor semi-automated fashion. By contrast, lead discovery applications typically focuson one or a few well-characterized targets.Despite these challenges, this approach has recently begun to receive considerableattention (Macchiarulo et al. 2004; Paul et al. 2004; Kalyanaraman et al. 2005;Tyagi and Pleiss 2006; Favia et al. 2008). In two published examples of functionprediction by docking (Hermann et al. 2007; Song et al. 2007), the protein of interestwas first identified as a member of a functionally diverse superfamily ofenzymes. This allowed narrowing the search space of potential substrates. However,different approaches were used in each study to obtain accurate rankings of thedocked molecules.One study focused on a family of proteins that, based on sequence information,could be recognized as members of the enolase superfamily but not reliablyannotated with more detailed functions (Song et al. 2007). Members of theenolase superfamily all catalyze abstraction of a proton from a carbon adjacent toa carboxylate group, but their substrates and overall reactions vary significantly(Babbitt and Gerlt 2000). As no experimentally determined structures were availablefor the family, a homology model was constructed for one of the sequencesbased on the most similar known structure (35% sequence identity), an Ala-Gluepimerase. N-succinyl amino acid racemases also clustered near the unknownfamily in sequence similarity space, so dipeptides and N-succinyl amino acids wereincluded in the libraries for computational and experimental screening. Despite
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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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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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Chapter 6Function Diversity Within
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6 Function Diversity Within Folds a
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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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Page 210 and 211: 8 3D Motifs 203In addition to SITE
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Page 222 and 223: 8 3D Motifs 215Ivanisenko VA, Pintu
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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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