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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8233889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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