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100 T. Nugent and D.T. JonesTable 4.3 Machine learning-based alpha-helical TM topology predictors. MSA: Topology predictionsmade using multiple sequence alignments. HGA: Suitable for whole genome analysisMethod URL Algorithm FeaturesMEMSAT3 http://bioinf.cs.ucl.ac.uk/psipred/ NN Signal peptide,MSA, HGAMINNOU http://minnou.cchmc.org/ NN MSAPHDhtm http://www.predictprotein.org/ NN Signal peptide,MSA, constrainedPhobius http://phobius.sbc.su.se/ HMM HGATMHMMhttp://www.cbs.dtu.dk/services/TMHMM/HMM Re-entrantregion, HGAPRODIV-TMHMM http://www.pdc.kth.se/~hakanv/ HMM Constrainedprodiv-tmhmm/HMMTOP http://www.enzim.hu/hmmtop/ HMM MSAENSEMBLE http://pongo.biocomp.unibo. NN + HMM Re-entrant regionit/pongo/OCTOPUS http://octopus.cbr.su.se/ NN + HMM ConsensusSVMtophttp://bio-cluster.iis.sinica.edu. SVM Consensustw/~bioapp/SVMtop/PONGOhttp://pongo.biocomp.unibo. Multipleit/pongo/BPROMPT http://www.jenner.ac.uk/bprompt/ MultipleMore recently, Support Vector Machines (SVMs) have been applied to TM proteintopology prediction (Yuan et al. 2004; Lo et al. 2008). While NNs and HMMsare capable of producing multiple outputs, SVMs are binary classifiers thereforemultiple SVMs must be employed to classify the numerous residue preferencesbefore being combined into a probabilistic framework. Although multiclass rankingSVMs do exist, they are generally considered unreliable since in many cases nosingle mathematical function exists to separate all classes of data from one another.However, SVMs are capable of learning complex relationships among the aminoacids within a given window with which they are trained, particularly when providedwith evolutionary information, and are also more resilient to the problem ofover-training compared to other machine learning methods, although numerousadjustable parameters can result in optimisation becoming extremely timeconsuming.4.6.1.2 Consensus ApproachesA number of methods now combine multiple machine learning approaches.ENSEMBLE (Martelli et al. 2003) uses a NN and two HMMs, while OCTOPUS(Viklund and Elofsson 2008) uses two sets of four NNs and one HMM. Both groupsreport higher prediction accuracies compared with methods based on only a single
4 Membrane Protein Structure Prediction 101classification algorithm. BPROMPT (Taylor et al. 2003), which takes a consensusapproach, combines the outputs of five different predictors to produce an overalltopology using a Bayesian belief network, while Nilsson et al. (2002) used a simplemajority-vote approach to return the best topology from their five predictors. ThePONGO server (Amico et al. 2006) returns the results of five high scoring methodsin a graphical format for direct comparison. In most cases, but particularly proteinswhose topology is not straightforward, considering a number of predictions by differentmethods is highly advisable (Fig. 4.4).4.6.1.3 Signal Peptides and Re-Entrant HelicesOne problem faced by modern topology predictors is the discrimination betweenTM helices and other features composed largely of hydrophobic residues. Theseinclude targeting motifs such as signal peptides and signal anchors, amphipathichelices, and re-entrant helices – membrane penetrating helices that enter and exitthe membrane on the same side, common in many ion channel families (Fig. 4.5).The high similarity between such features and the hydrophobic profile of a TMhelix frequently leads to crossover between the different types of predictions.Should these elements be predicted as TM helices, the ensuing topology predictionis likely to be severely disrupted. Some prediction methods, such as SignalP(Bendtsen et al. 2004) and TargetP (Emanuelsson et al. 2007), are effective in identifyingsignal peptides, and may be used as a pre-filter prior to analysis using a TMtopology predictor. Phobius (Käll et al. 2004) uses a HMM to successfully addressthe problem of signal peptides in TM protein topology prediction, whilePolyPhobius (Käll et al. 2005) further increases accuracy by including homologyinformation. Other methods such as TOP-MOD (Viklund et al. 2006) andOCTOPUS have attempted to incorporate identification of re-entrant regions into aTM topology predictor but there is significant room for improvement. The problem,particularly regarding re-entrant helices, is the lack of reliable data with which totrain machine-learning based methods.Fig. 4.4 Using a number of methods to form a consensus topology prediction
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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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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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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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