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The Two-Step Clustering Approach for Metastable States Learning

Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein–protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were propose...

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Detalles Bibliográficos
Autores principales: Jiang, Hangjin, Fan, Xiaodan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233889/
https://www.ncbi.nlm.nih.gov/pubmed/34205252
http://dx.doi.org/10.3390/ijms22126576
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author Jiang, Hangjin
Fan, Xiaodan
author_facet Jiang, Hangjin
Fan, Xiaodan
author_sort Jiang, Hangjin
collection PubMed
description Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein–protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.
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spelling pubmed-82338892021-06-27 The Two-Step Clustering Approach for Metastable States Learning Jiang, Hangjin Fan, Xiaodan Int J Mol Sci Review Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein–protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area. MDPI 2021-06-19 /pmc/articles/PMC8233889/ /pubmed/34205252 http://dx.doi.org/10.3390/ijms22126576 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Jiang, Hangjin
Fan, Xiaodan
The Two-Step Clustering Approach for Metastable States Learning
title The Two-Step Clustering Approach for Metastable States Learning
title_full The Two-Step Clustering Approach for Metastable States Learning
title_fullStr The Two-Step Clustering Approach for Metastable States Learning
title_full_unstemmed The Two-Step Clustering Approach for Metastable States Learning
title_short The Two-Step Clustering Approach for Metastable States Learning
title_sort two-step clustering approach for metastable states learning
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233889/
https://www.ncbi.nlm.nih.gov/pubmed/34205252
http://dx.doi.org/10.3390/ijms22126576
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