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106 T. Nugent and D.T. Jonessubdivided into four steps: template selection, target-template alignment, model constructionand model assessment, all of which can be performed iteratively in order toimprove the quality of the final model (Sánchez and Sali 1997; Martí-Renom et al.2000). A selection of homology modelling programs are shown in Table 4.5.Aside from SWISS-MODEL (Peitsch 1996) which has a 7TM/GPCR interface,none of the methods in Table 4.5 are specifically designed to deal with TM proteins.Care must therefore be taken to ensure that models do not contain polar side chainsthat protrude into the hydrophobic membrane region. Specific side chain modellingtools such as SCWRL (Canutescu et al. 2003) may suffer from this same problem,though the accuracy of extramembranous regions of the model is likely to increase.Despite the lack of TM protein specific modelling tools, recent research has demonstratedthat bioinformatics tools currently applied to soluble proteins, from profilematching to secondary structure prediction and homology modelling, perform atleast as well on TM proteins (Forrest et al. 2006). Indeed, an important applicationof TM protein modelling lies in the identification and validation of drug targets, aswell as the identification and optimisation of lead compounds. Homology modelbaseddrug design has been applied to a number of kinases including epidermalTable 4.5 A selection of commonly used homology modelling programs (Adapted from Wallnerand Elofsson 2005)Program Description URLModellerSegMod/ENCADSWISS-MODEL3D-JIGSAWNestBuilderJackalSCWRL3Modelling by satisfying spatialrestraints. Includes denovo loop modelling.Modelling by segment matchingcombined with moleculardynamics refinement.Web server modelling byrigid-body assembly.Web server modelling serverwith energy minimisationusing CHARMM.Multiple template-basedmodelling using an artificialevolution method.Self Consistent Mean-Fieldtheory (SCMF) (Koehland Delarue 1996)approach for loop andside chain modelling.Modelling using a selectionof different programs.Backbone-dependent rotamerlibrary-based side chainmodelling.http://www.salilab.org/modeller/http://csb.stanford.edu/levitt/segmod/http://swissmodel.expasy.org/http://bmm.cancerresearchuk.org/~3djigsaw/http://www.charmm.org/http://wiki.c2b2.columbia.edu/honiglab_public/On request: koehl@cs.ucdavis.eduhttp://wiki.c2b2.columbia.edu/honiglab_public/index.phphttp://dunbrack.fccc.edu/SCWRL3.php
4 Membrane Protein Structure Prediction 107growth factor-receptor tyrosine kinase protein (Ghosh et al. 2001), Bruton’s tyrosinekinase (Mahajan et al. 1999) and Janus kinase 3 (Sudbeck et al. 1999).Ab initio modelling, or de novo modelling, involves the construction of a 3Dmodel in the absence of any structural data relating to the target protein or ahomologue. Research has focused in three main areas: alternate lower-resolutionrepresentations of proteins, accurate energy functions, and efficient sampling methods.While most methods address globular proteins, some efforts have been directedat TM protein structure prediction.ROSETTA (Rohl et al. 2004) is an ab initio modelling server that uses potentialfunctions for computing the lowest energy structure for an amino acid sequence.Feedback from the prediction is used continually to improve potential functions andsearch algorithms. A modified version of the ROSETTA algorithm (Barth et al.2007) uses an energy function that describes membrane intraprotein interactions atatomic level and membrane protein/lipid interactions implicitly, while treatinghydrogen bonds explicitly. Results suggest that the model captures the essentialphysical properties that govern the solvation and stability of membrane proteins,allowing the structures of small TM protein domain (
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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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Page 72 and 73: 62 A. FiserTable 3.1 (continued)SWI
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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
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6 Function Diversity Within Folds a
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Chapter 7Predicting Protein Functio
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7 Predicting Protein Function from
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Table 7.1 Online resources and tool
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Chapter 83D MotifsElaine C. Meng, B
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8 3D Motifs 189clustering similar s
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8 3D Motifs 191To improve the signa
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8 3D Motifs 193structures together.
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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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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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