From Protein Structure to Function with Bioinformatics.pdf

2 Fold Recognition 472.4 Alignment Accuracy, Model Qualityand Statistical SignificanceFold recognition can be divided into two problems (1) detection of an appropriatetemplate and (2) alignment to that template. Clearly any useful method fortemplate-based protein structure prediction must at minimum be able to detectappropriate templates. However, regardless of the quality of template detection,the quality of the alignment completely determines the quality of the resultingmodel. Errors in this alignment, despite using a good template, will still lead topoor models.Until this point, with the exception of some of the SVM classifiers discussed inSection 3.3, we have assumed that a system capable of accurate detection of templateswill also generate an accurate alignment. Although often a reasonableassumption, there will be many cases where this does not hold. Firstly, most of thesystems described essentially rank templates by some alignment score. In otherwords, there will always be a ‘top scoring model’. Simply because one alignmentis better scoring than all the others does not imply the alignment is error-free.Secondly, most of these methods are attempting to detect extremely remote signalsof homology. As such, they may simply detect certain conserved motifs, or patchesof similarity, interrupted by long stretches of sequence where similarity is undetectableand thus the alignment is essentially ‘noise’. This in turn leads to large errorsin the resulting three-dimensional model.For these reasons many groups have investigated techniques to improve alignmentaccuracy and predict the quality of the resulting model. There are three waysin which one can tackle the problem of alignment accuracy: (1) improve the algorithmfor alignment generation directly (2) generate many alignments and developa system to pick the best one and (3) build 3D models from many alignments andassess the resulting models.2.4.1 Algorithms for Alignment Generation and AssessmentWe have already seen how using evolutionary information in the form of profiles orHMMs and predicted secondary structure improves homologue detection and thisis usually accompanied by a similar improvement in alignment accuracy (Elofsson2002). Zachariah et al. (2005) demonstrated that a more elaborate model for gapinitiation and extension during alignment by dynamic programming did notimprove homologue detection, but did increase alignment accuracy significantly.A successful approach in recent CASPs has been that of Venclovas andMargelevicius (2005). In this procedure, a set of sequences that bridge sequencespace between the query sequence and template(s) are used to initiate additionalPSI-BLAST searches against the nonredundant sequence database. Query–templatesequence alignments are then extracted from search results and their