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Journal Article

Context similarity scoring improves protein sequence alignments in the midnight zone.

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Söding,  J.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Meier, A., & Söding, J. (2015). Context similarity scoring improves protein sequence alignments in the midnight zone. Bioinformatics, 31(5), 674-681. doi:10.1093/bioinformatics/btu697.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-4D7C-7
Abstract
Motivation: High-quality protein sequence alignments are essential for a number of downstream applications such as template-based protein structure prediction. In addition to the similarity score between sequence profile columns, many current profile–profile alignment tools use extra terms that compare 1D-structural properties such as secondary structure and solvent accessibility, which are predicted from short profile windows around each sequence position. Such scores add non-redundant information by evaluating the conservation of local patterns of hydrophobicity and other amino acid properties and thus exploiting correlations between profile columns. Results: Here, instead of predicting and comparing known 1D properties, we follow an agnostic approach. We learn in an unsupervised fashion a set of maximally conserved patterns represented by 13-residue sequence profiles, without the need to know the cause of the conservation of these patterns. We use a maximum likelihood approach to train a set of 32 such profiles that can best represent patterns conserved within pairs of remotely homologs, structurally aligned training profiles. We include the new context score into our Hmm-Hmm alignment tool hhsearch and improve especially the quality of difficult alignments significantly. CONCLUSION: The context similarity score improves the quality of homology models and other methods that depend on accurate pairwise alignments.