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Inferring interaction partners from protein sequences using mutual information
Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partner...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258550/ https://www.ncbi.nlm.nih.gov/pubmed/30422978 http://dx.doi.org/10.1371/journal.pcbi.1006401 |
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author | Bitbol, Anne-Florence |
author_facet | Bitbol, Anne-Florence |
author_sort | Bitbol, Anne-Florence |
collection | PubMed |
description | Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins. |
format | Online Article Text |
id | pubmed-6258550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62585502018-12-06 Inferring interaction partners from protein sequences using mutual information Bitbol, Anne-Florence PLoS Comput Biol Research Article Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins. Public Library of Science 2018-11-13 /pmc/articles/PMC6258550/ /pubmed/30422978 http://dx.doi.org/10.1371/journal.pcbi.1006401 Text en © 2018 Anne-Florence Bitbol http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bitbol, Anne-Florence Inferring interaction partners from protein sequences using mutual information |
title | Inferring interaction partners from protein sequences using mutual information |
title_full | Inferring interaction partners from protein sequences using mutual information |
title_fullStr | Inferring interaction partners from protein sequences using mutual information |
title_full_unstemmed | Inferring interaction partners from protein sequences using mutual information |
title_short | Inferring interaction partners from protein sequences using mutual information |
title_sort | inferring interaction partners from protein sequences using mutual information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258550/ https://www.ncbi.nlm.nih.gov/pubmed/30422978 http://dx.doi.org/10.1371/journal.pcbi.1006401 |
work_keys_str_mv | AT bitbolanneflorence inferringinteractionpartnersfromproteinsequencesusingmutualinformation |