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Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics

Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample h...

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Detalles Bibliográficos
Autores principales: Bonomi, Massimiliano, Camilloni, Carlo, Vendruscolo, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999896/
https://www.ncbi.nlm.nih.gov/pubmed/27561930
http://dx.doi.org/10.1038/srep31232
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author Bonomi, Massimiliano
Camilloni, Carlo
Vendruscolo, Michele
author_facet Bonomi, Massimiliano
Camilloni, Carlo
Vendruscolo, Michele
author_sort Bonomi, Massimiliano
collection PubMed
description Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample heterogeneity. The study of complex macromolecular systems, however, requires an extensive conformational sampling, which represents a separate challenge. To address such challenge and to exhaustively and efficiently generate structural ensembles we combine metainference with metadynamics and illustrate its application to the calculation of the free energy landscape of the alanine dipeptide.
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spelling pubmed-49998962016-09-07 Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics Bonomi, Massimiliano Camilloni, Carlo Vendruscolo, Michele Sci Rep Article Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample heterogeneity. The study of complex macromolecular systems, however, requires an extensive conformational sampling, which represents a separate challenge. To address such challenge and to exhaustively and efficiently generate structural ensembles we combine metainference with metadynamics and illustrate its application to the calculation of the free energy landscape of the alanine dipeptide. Nature Publishing Group 2016-08-26 /pmc/articles/PMC4999896/ /pubmed/27561930 http://dx.doi.org/10.1038/srep31232 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
Bonomi, Massimiliano
Camilloni, Carlo
Vendruscolo, Michele
Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title_full Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title_fullStr Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title_full_unstemmed Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title_short Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics
title_sort metadynamic metainference: enhanced sampling of the metainference ensemble using metadynamics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999896/
https://www.ncbi.nlm.nih.gov/pubmed/27561930
http://dx.doi.org/10.1038/srep31232
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