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GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects
The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805226/ https://www.ncbi.nlm.nih.gov/pubmed/31406981 http://dx.doi.org/10.1093/molbev/msz179 |
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author | Laine, Elodie Karami, Yasaman Carbone, Alessandra |
author_facet | Laine, Elodie Karami, Yasaman Carbone, Alessandra |
author_sort | Laine, Elodie |
collection | PubMed |
description | The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/. |
format | Online Article Text |
id | pubmed-6805226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-68052262019-10-25 GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects Laine, Elodie Karami, Yasaman Carbone, Alessandra Mol Biol Evol Methods The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/. Oxford University Press 2019-11 2019-08-12 /pmc/articles/PMC6805226/ /pubmed/31406981 http://dx.doi.org/10.1093/molbev/msz179 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Laine, Elodie Karami, Yasaman Carbone, Alessandra GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title | GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title_full | GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title_fullStr | GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title_full_unstemmed | GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title_short | GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects |
title_sort | gemme: a simple and fast global epistatic model predicting mutational effects |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805226/ https://www.ncbi.nlm.nih.gov/pubmed/31406981 http://dx.doi.org/10.1093/molbev/msz179 |
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