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Improving landscape inference by integrating heterogeneous data in the inverse Ising problem

The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are in...

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Autores principales: Barrat-Charlaix, Pierre, Figliuzzi, Matteo, Weigt, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122905/
https://www.ncbi.nlm.nih.gov/pubmed/27886273
http://dx.doi.org/10.1038/srep37812
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author Barrat-Charlaix, Pierre
Figliuzzi, Matteo
Weigt, Martin
author_facet Barrat-Charlaix, Pierre
Figliuzzi, Matteo
Weigt, Martin
author_sort Barrat-Charlaix, Pierre
collection PubMed
description The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences.
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spelling pubmed-51229052016-12-07 Improving landscape inference by integrating heterogeneous data in the inverse Ising problem Barrat-Charlaix, Pierre Figliuzzi, Matteo Weigt, Martin Sci Rep Article The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences. Nature Publishing Group 2016-11-25 /pmc/articles/PMC5122905/ /pubmed/27886273 http://dx.doi.org/10.1038/srep37812 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Barrat-Charlaix, Pierre
Figliuzzi, Matteo
Weigt, Martin
Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title_full Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title_fullStr Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title_full_unstemmed Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title_short Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
title_sort improving landscape inference by integrating heterogeneous data in the inverse ising problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122905/
https://www.ncbi.nlm.nih.gov/pubmed/27886273
http://dx.doi.org/10.1038/srep37812
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