Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Laine, Elodie, Karami, Yasaman, Carbone, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
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
_version_ 1783461332809416704
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
work_keys_str_mv AT laineelodie gemmeasimpleandfastglobalepistaticmodelpredictingmutationaleffects
AT karamiyasaman gemmeasimpleandfastglobalepistaticmodelpredictingmutationaleffects
AT carbonealessandra gemmeasimpleandfastglobalepistaticmodelpredictingmutationaleffects