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Estimating probabilistic context-free grammars for proteins using contact map constraints

Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular represent...

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Autores principales: Dyrka, Witold, Pyzik, Mateusz, Coste, François, Talibart, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428041/
https://www.ncbi.nlm.nih.gov/pubmed/30918754
http://dx.doi.org/10.7717/peerj.6559
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author Dyrka, Witold
Pyzik, Mateusz
Coste, François
Talibart, Hugo
author_facet Dyrka, Witold
Pyzik, Mateusz
Coste, François
Talibart, Hugo
author_sort Dyrka, Witold
collection PubMed
description Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between the protein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to facilitate the training of probabilistic context-free grammars representing families of protein sequences. We develop the theory behind the introduction of contact constraints in maximum-likelihood and contrastive estimation schemes and implement it in a machine learning framework for protein grammars. The proposed framework is tested on samples of protein motifs in comparison with learning without contact constraints. The evaluation shows high fidelity of grammatical descriptors to protein structures and improved precision in recognizing sequences. Finally, we present an example of using our method in a practical setting and demonstrate its potential beyond the current state of the art by creating a grammatical model of a meta-family of protein motifs. We conclude that the current piece of research is a significant step towards more flexible and accurate modeling of collections of protein sequences. The software package is made available to the community.
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spelling pubmed-64280412019-03-27 Estimating probabilistic context-free grammars for proteins using contact map constraints Dyrka, Witold Pyzik, Mateusz Coste, François Talibart, Hugo PeerJ Bioinformatics Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between the protein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to facilitate the training of probabilistic context-free grammars representing families of protein sequences. We develop the theory behind the introduction of contact constraints in maximum-likelihood and contrastive estimation schemes and implement it in a machine learning framework for protein grammars. The proposed framework is tested on samples of protein motifs in comparison with learning without contact constraints. The evaluation shows high fidelity of grammatical descriptors to protein structures and improved precision in recognizing sequences. Finally, we present an example of using our method in a practical setting and demonstrate its potential beyond the current state of the art by creating a grammatical model of a meta-family of protein motifs. We conclude that the current piece of research is a significant step towards more flexible and accurate modeling of collections of protein sequences. The software package is made available to the community. PeerJ Inc. 2019-03-18 /pmc/articles/PMC6428041/ /pubmed/30918754 http://dx.doi.org/10.7717/peerj.6559 Text en ©2019 Dyrka et al. 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Dyrka, Witold
Pyzik, Mateusz
Coste, François
Talibart, Hugo
Estimating probabilistic context-free grammars for proteins using contact map constraints
title Estimating probabilistic context-free grammars for proteins using contact map constraints
title_full Estimating probabilistic context-free grammars for proteins using contact map constraints
title_fullStr Estimating probabilistic context-free grammars for proteins using contact map constraints
title_full_unstemmed Estimating probabilistic context-free grammars for proteins using contact map constraints
title_short Estimating probabilistic context-free grammars for proteins using contact map constraints
title_sort estimating probabilistic context-free grammars for proteins using contact map constraints
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428041/
https://www.ncbi.nlm.nih.gov/pubmed/30918754
http://dx.doi.org/10.7717/peerj.6559
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