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From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes
BACKGROUND: The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its rela...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156908/ https://www.ncbi.nlm.nih.gov/pubmed/30255791 http://dx.doi.org/10.1186/s12864-018-5024-z |
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author | Qiao, Wanli Akhter, Nasrin Fang, Xiaowen Maximova, Tatiana Plaku, Erion Shehu, Amarda |
author_facet | Qiao, Wanli Akhter, Nasrin Fang, Xiaowen Maximova, Tatiana Plaku, Erion Shehu, Amarda |
author_sort | Qiao, Wanli |
collection | PubMed |
description | BACKGROUND: The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function. RESULTS: Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation. CONCLUSIONS: The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction. |
format | Online Article Text |
id | pubmed-6156908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61569082018-09-27 From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes Qiao, Wanli Akhter, Nasrin Fang, Xiaowen Maximova, Tatiana Plaku, Erion Shehu, Amarda BMC Genomics Research BACKGROUND: The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function. RESULTS: Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation. CONCLUSIONS: The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction. BioMed Central 2018-09-24 /pmc/articles/PMC6156908/ /pubmed/30255791 http://dx.doi.org/10.1186/s12864-018-5024-z Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Qiao, Wanli Akhter, Nasrin Fang, Xiaowen Maximova, Tatiana Plaku, Erion Shehu, Amarda From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title | From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title_full | From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title_fullStr | From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title_full_unstemmed | From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title_short | From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
title_sort | from mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156908/ https://www.ncbi.nlm.nih.gov/pubmed/30255791 http://dx.doi.org/10.1186/s12864-018-5024-z |
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