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2 Fold Recognition 39based on the returned alignment then one would have one of the simplest proteinstructure prediction techniques (Fig. 2.4a).The obvious shortcoming of this approach is the limited ability of the simple20 × 20 scoring functions to detect anything but close (>30% sequence identity)homology. Given that we know sequences can diverge well below this threshold ofsequence identity whilst maintaining highly similar structures, it was clear thatthere would be many homologous relationships we were missing with thisapproach, and which, if detectable, would permit a substantial increase in our abilityto predict structure.2.3.1 Using Predicted Structural FeaturesOne of the earliest attempts at pushing homology recognition beyond simplesequence matching was developed by Bowie et al. (1991). The idea is based on thefact that certain structural features of a protein sequence can be predicted in theabsence of an explicit template. Most notably, the secondary structure, i.e. the locationsof alpha-helices and beta-strands, can now be predicted with an accuracyapproaching 80% using programs such as PSIPRED (Jones 1999a). Given thatstructure is more conserved than sequence, a pair of remotely homologous proteinswill contain similar patterns of secondary structure elements even in the absence ofany obvious sequence similarity. In addition, the solvent exposure of a residue canbe predicted with relatively high accuracy (e.g. Kim and Park 2003), as can thepresence of tight beta-hairpin turns (e.g. Kumar et al. 2005).These predicted structural features provide us with further information that canbe used together with the sequence matching. When aligning two amino acidsfrom the query and template one can calculate a compatibility score based on amutation matrix such as BLOSUM plus terms involving secondary structurematching and solvent exposure:S = Seq + SS + Solvij ij ij ijWhere S ijis the overall score for matching residue i in the query sequence with residuej in the template sequence, Seq ijis the score from the BLOSUM matrix formatching i and j, SS ijis the score for matching the predicted secondary structuretype at residue i with the known secondary structure at residue j, and Solv ijis thescore for matching the burial state predicted for residue i with the known burialstate at residue j. Simple versions of such scoring functions are depicted in Table2.1b and c, where identical states (helix matched to helix for example) receive ascore of +1 and all other combinations receive −1. Often the functions will be moreelaborate and be based on empirical observation of the frequency with whichthe different states tend to be aligned in known homologues. This is analogous tothe progression from a simple identity-based sequence matching matrix towards themore sensitive BLOSUM-style matrix.
40 L.A. KelleyFig. 2.4 Schematic representation of the progress of sequence-based fold recognition techniquesover time. The leftmost part of each figure represents the query sequence. The grey box to the rightof each figure indicates a database of templates of known structure. The arrows indicate a comparisonbetween the query and a particular template. (a) Simple comparison of an amino acidsequence against a database of sequences. (b) Including predicted (query) and known (template)secondary structure information in the matching. Wavy lines indicate alpha-helices and lozengesindicate beta-strands. (c) Here the query is represented by a profile of multiple sequences, a PSSMor a Hidden Markov Model (the coloured grid). Each row of the grid represents a different homologoussequence, each column represents a different sequence position. (d) The inverse of(c) where now a query sequence is searched against a library of profiles. (e) Profile-profile comparison(still with a simple 3-state string representing secondary structure. (f) as (e) but now with
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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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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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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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