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62 A. FiserTable 3.1 (continued)SWISS-MODELFAMSWHATIFPUDGE3D-JURYRAPPERESYPRED3DCONSENSUSPCONSLoop modellingARCHPREDMODLOOPWLOOPSide chain modellingSCWRLIRECSModel evaluationPROCHECKProsa-webWHATCHECKVERIFY3DANOLEAAQUAPROQswissmodel.expasy.org/workspacewww.pharm.kitasato-u.ac.jp/famswww.cmbi.kun.nl/whatif/wiki.c2b2.columbia.edu/honiglab_public/index.php/Softwaremeta.bioinfo.plmordred.bioc.cam.ac.uk/~rapperwww.fundp.ac.be/sciences/biologie/urbm/bioinfo/esypred/structure.bu.edu/cgi-bin/consensus/consensus.cgipcons.netfiserlab.org/servers/archpredsalilab.org/modloopbioserv.rpbs.jussieu.fr/cgi-bin/WLoopdunbrack.fccc.edu/SCWRL3.phpirecs.bioinf.mpi-inf.mpg.de/index.phpwww.biochem.ucl.ac.uk/~roman/procheck/procheck.htmlprosa.services.came.sbg.ac.at/prosa.phpswift.cmbi.ru.nl/gv/whatchecknihserver.mbi.ucla.edu/Verify_3Dprotein.bio.puc.cl/cardex/servers/anolea/urchin.bmrb.wisc.edu/~jurgen/Aqua/server/www.sbc.su.se/~bjornw/ProQ/ProQ.cgiand biologically most relevant templates, optimally combining multiple templateinformation, refining alignments in non trivial cases, selecting segments for loopmodelling, including cofactors and ligands in the model or specifying externalrestraints require an expert knowledge that is difficult to fully automate (Fiser andSali 2003a) although more and more efforts on automation point to this direction(Contreras-Moreira et al. 2003; Fernandez-Fuentes et al. 2007b).3.2.1 Searching for Structures Related to the Target SequenceComparative modelling usually starts by searching the Protein Data Bank (PDB)(Berman et al. 2007) of known protein structures using the target sequence as thequery. This search is generally done by comparing the target sequence with thesequence of each of the structures in the database.There are two main classes of protein comparison methods that are useful in foldidentification. The first class compares the sequences of the target with each of thedatabase templates independently. This can be done by using pairwise sequencesequencecomparison (Apostolico and Giancarlo 1998). The performance of thesemethods in sequence searching (Pearson 2000; Sauder et al. 2000) and fold assignments(Brenner et al. 1998) has been evaluated exhaustively. The most popular
3 Comparative Protein Structure Modelling 63programs in the class include FASTA (Pearson 2000) and BLAST (Schaffer et al.2001). To improve the sensitivity of the sequence based searches evolutionaryinformation can be incorporated in form of multiple sequence alignment (Altschulet al. 1997; Henikoff et al. 2000; Krogh et al. 1994; Marti-Renom et al. 2004;Rychlewski et al. 2000). These approaches begin by finding all sequences in asequence database that are clearly related to the target and easily aligned with it.The multiple alignment of these sequences is the target sequence profile, whichimplicitly carries additional information about the location and pattern of evolutionaryconserved positions of the protein. The most well known program in thisclass is PSI-BLAST(Altschul et al. 1997), which implements a heuristic searchalgorithm for short motifs. A further step to increase the sensitivity of this approachis to pre-calculate sequence profiles for all the known structures and then use pairwisedynamic programming algorithm to compare the two profiles. This has beenimplemented, among other programs, in COACH (Edgar and Sjolander 2004) andin FFAS03 (Jaroszewski et al. 1998, 2005). The construction of profile-basedHidden Markov Models (HMM) is another sensitive way to locate universally conservedmotifs among sequences (Karplus et al. 1998). A substantial improvementin HMM approaches was achieved by incorporating information about predictedsecondary structural elements (Karchin et al. 2003; Karplus et al. 2005). Anotherdevelopment in this group of methods is the phylogenetic tree-driven HMM, whichselects a different subset of sequences for profile HMM analysis at each node in theevolutionary tree (Edgar and Sjolander 2003). Locating sequence intermediates thatare homologous to both sequences may also enhance the template searches (Johnand Sali 2004; Sauder et al. 2000). These more sensitive fold identification techniquesare especially useful for finding significant structural relationships whensequence identity between the target and the template drops below 25%. Moreaccurate sequence profiles and structural alignments can be constructed with consistency-basedapproaches such as T-Coffee (Moretti et al. 2007) PROMAL (andPROMAL3D for structures) (Pei and Grishin 2007; Pei et al. 2008), ProbCons (Doet al. 2005) etc. For recent reviews of multiple sequence alignments see (Edgar andBatzoglou 2006; Notredame 2007).The second class of methods relies on pairwise comparison of a proteinsequence and a protein structure; the target sequence is matched against alibrary of 3D profiles or threaded through a library of 3D folds. These methodsare also called fold assignment, threading or 3D template matching (Bowieet al. 1991; Finkelstein and Reva 1991; Jaroszewski et al. 1998; Jones 1999; Shiet al. 2001; Sippl 1995). These methods, discussed in detail in Chapter 2, areespecially useful when sequence profiles are not possible to construct becausethere are not enough known sequences that are clearly related to the target orpotential templates.Template search methods “outperform” the needs of comparative modelling inthe sense that they are able to locate sequences that are so remotely related as torender construction of a reliable comparative model impossible. The reason for thisis that sequence relationships are often established on short conserved segments,while a successful comparative modelling exercise requires an overall correct alignmentfor the entire modelled part of the protein. This is an important distinction
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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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Page 38 and 39: Chapter 2Fold RecognitionLawrence A
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Page 64 and 65: 2 Fold Recognition 53Berman HM, Wes
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Page 68 and 69: 58 A. Fiserwith more than 50% seque
Page 70 and 71: 60 A. FiserIn contrast to ab initio
Page 74 and 75: 64 A. Fiserbetween fold recognition
Page 76 and 77: 66 A. FiserFig. 3.1 Comparing accur
Page 78 and 79: 68 A. Fiserinto conserved core regi
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Page 88 and 89: 78 A. FiserA rigorous statistical e
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Page 92 and 93: 82 A. FiserImproved and new methods
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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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