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60 A. FiserIn contrast to ab initio techniques comparative protein structure modellingusually provides models that are comparable to low resolution X-ray crystallographyor medium resolution NMR solution structures. However, its applicabilityis limited to those sequences that can be confidently mapped to known structures.Currently, the probability of finding related proteins of known structurefor a sequence picked randomly from a genome ranges approximately from 30%to 80%, depending on the genome. Approximately 70% of all known sequenceshave at least one domain that is detectably related to at least one protein ofknown structure (Pieper et al. 2006). This fraction is more than an order of magnitudelarger than the number of experimentally determined protein structuresdeposited in the Protein Data Bank (Berman et al. 2007). The applicability ofcomparative modelling is steadily increasing because the increasing number ofexperimentally determined novel structures. This trend is accentuated by theProtein Structure Initiative (PSI) project that aims to determine at least onestructure for each protein families (Burley et al. 2008; Vitkup et al. 2001). Aftera five year period of feasibility tests and technology build-up (PSI-1, years2000–2005), these structural genomics efforts are now in “production phase”(PSI-2, years 2005–2010) and it is conceivable that their aim will be substantiallyachieved in less than 10 years, making comparative modelling applicableto most protein sequences.As we will see, in practice, template based modelling always includes informationthat is independent from the template, in form of various force restraints fromgeneral statistical observations or molecular mechanical force fields. As a consequenceof improving forcefields and search algorithms the most successfulapproaches are more and more often explore template independent conformationalspace (R. Das et al. 2007; Y. Zhang 2007). Similarly, the most successful ab initioapproaches, in fact, are using fragments of known structures to build up models(Bystroff and Baker 1998; Zhou et al. 2007). While it makes sense to discuss thetwo fundamental principles behind the techniques employed in structure modellingseparately, the current trends are pointing to approaches that extensively combineboth. While truly ab initio approaches can shed light on the dynamics of the actualfolding process, in practice, effective structure modelling almost always involves acertain flavour of template-based modelling.3.2 Steps in Comparative Protein Structure ModellingComparative or homology (template-based) protein structure modelling builds athree-dimensional model for a protein of unknown structure (the target) based onone or more related proteins of known structure (the templates) (Blundell et al.1987; Fiser 2004; Ginalski 2006; Greer 1981; Marti-Renom et al. 2000; Petrey andHonig 2005). The necessary conditions for getting a useful model are (i) detectablesimilarity between the target sequence and the sequence of the template structureand (ii) availability of a correct alignment between them.
3 Comparative Protein Structure Modelling 61All current comparative modelling methods consist of five sequential steps. Thefirst step is to search for proteins with known 3D structures that are related to thetarget sequence. The second step is to pick those structures that will be used as templates.The third step is to align their sequences with the target sequence. The fourthstep is to build the model for the target sequence given its alignment with the templatestructures. The last step is to evaluate the model, using a variety of criteria.There are several computer programs and web servers that automate the comparativemodelling process (Table 3.1). While the web servers are convenient anduseful (Battey et al. 2007; Fernandez-Fuentes et al. 2007a; Rai et al. 2006; Y. Zhang2007), the best results are still obtained by non-automated, expert use of the variousmodelling tools (Kopp et al. 2007). Complex decisions for selecting the structurallyTable 3.1 Names and URLs of some online tools useful for various aspects of comparativemodellingFold recognition by database searchesBLAST/PSI-BLAST www.ncbi.nlm.nih.gov/BLAST/FastA/SSEARCHwww.ebi.ac.uk/fasta33FFAS03ffas.ljcrf.edu/ffas-cgi/cgi/ffas.plFold recognition by threadingPHYRE/3D-PSSMwww.sbg.bio.ic.ac.uk/~3dpssmFUGUEwww-cryst.bioc.cam.ac.uk/~fugueLOOPPcbsuapps.tc.cornell.edu/MUSTERzhang.bioinformatics.ku.edu/MUSTERSAM-T06www.soe.ucsc.edu/research/compbio/SAM_T06/T06-query.htmlProspectcompbio.ornl.gov/structure/prospectPSIPREDbioinf.cs.ucl.ac.uk/psipred/psiform.htmlUCLA-DOEwww.doe-mbi.ucla.edu/Services/FOLD123D123d.ncifcrf.govSequence alignment toolsSmith-Watermanjaligner.sourceforge.net/ClustalWwww.ebi.ac.uk/clustalw/MUSCLEwww.drive5.com/lobster/T-COFFEEtcoffee.vital-it.chPROMALSprodata.swmed.edu/promals/promals.phpPROBCONSprobcons.stanford.eduComparative modelling, loop and side chain modellingMMMwww.fiserlab.org/servers/MMMM4Twww.fiserlab.org/servers/M4TMODELLERwww.salilab.org/modeller/modeller.htmlMODWEBmodbase.compbio.ucsf.edu/ModWeb20-html/modweb.htmlI-TASSERzhang.bioinformatics.ku.edu/I-TASSER/HHPREDtoolkit.tuebingen.mpg.de/hhpred3D-JIGSAWwww.bmm.icnet.uk/servers/3djigsaw/CPH-MODELSwww.cbs.dtu.dk/services/CPHmodels/COMPOSERwww-cryst.bioc.cam.ac.uk(continued)
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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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Page 92 and 93: 82 A. FiserImproved and new methods
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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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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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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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