ArticleBiophysics and physicobiology2016
A unified statistical model of protein multiple sequence alignment integrating direct coupling and insertions.
Article in Biophysics and physicobiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.Bioinformatics (Oxford, England) · 2023Article
- Remote homology search with hidden Potts models.PLoS computational biology · 2020Article
- Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment.Biophysics and physicobiology · 2017Article
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Abstract
The multiple sequence alignment (MSA) of a protein family provides a wealth of information in terms of the conservation pattern of amino acid residues not only at each alignment site but also between distant sites. In order to statistically model the MSA incorporating both short-range and long-range correlations as well as insertions, I have derived a lattice gas model of the MSA based on the principle of maximum entropy. The partition function, obtained by the transfer matrix method with a mean-field approximation, accounts for all possible alignments with all possible sequences. The model parameters for short-range and long-range interactions were determined by a self-consistent condition and by a Gaussian approximation, respectively. Using this model with and without long-range interactions, I analyzed the globin and V-set domains by increasing the "temperature" and by "mutating" a site. The correlations between residue conservation and various measures of the system's stability indicate that the long-range interactions make the conservation pattern more specific to the structure, and increasingly stabilize better conserved residues.
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