Abstract
BackgroundProtein structure alignments are usually based on very different techniques to sequence alignments. We propose a method which treats sequence, structure and even combined sequence + structure in a single framework. Using a probabilistic approach, we calculate a similarity measure which can be applied to fragments containing only protein sequence, structure or both simultaneously.ResultsProof-of-concept results are given for the different problems. For sequence alignments, the methodology is no better than conventional methods. For structure alignments, the techniques are very fast, reliable and tolerant of a range of alignment parameters. Combined sequence and structure alignments may provide a more reliable alignment for pairs of proteins where pure structural alignments can be misled by repetitive elements or apparent symmetries.ConclusionThe probabilistic framework has an elegance in principle, merging sequence and structure descriptors into a single framework. It has a practical use in fast structural alignments and a potential use in finding those examples where sequence and structural similarities apparently disagree.
Highlights
Protein structure alignments are usually based on very different techniques to sequence alignments
Data sets The training data was a set of protein chains taken from the protein data bank (PDB) [40] such that no two members of the list had more than 50 % sequence identity [41]
The first measure was the Kullback-Leibler divergence, DKL given by Classification in general Fragment classifications were attempted for pure sequence, pure structure and combined sequence+structure and for fragment lengths up to k = 6
Summary
Protein structure alignments are usually based on very different techniques to sequence alignments. We calculate a similarity measure which can be applied to fragments containing only protein sequence, structure or both simultaneously. Protein sequence alignments usually rely on a substitution matrix. This reflects an evolutionary model and the probability that one type of residue has mutated to another [1,2]. Protein structures can be aligned, but using a very different set of heuristics. We propose a single framework which estimates the similarity of small protein fragments and can be applied to sequence, structure or both simultaneously. The benefit is that after the initial approximations, one has a rather rigorous measure of the similarity of pieces of proteins
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