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用于亚稳态学习的两步聚类方法。

The Two-Step Clustering Approach for Metastable States Learning.

作者信息

Jiang Hangjin, Fan Xiaodan

机构信息

Center for Data Science, Zhejiang University, Hangzhou 310058, China.

Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China.

出版信息

Int J Mol Sci. 2021 Jun 19;22(12):6576. doi: 10.3390/ijms22126576.

Abstract

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.

摘要

理解能量景观和构象动力学对于研究许多生物或化学过程至关重要,例如蛋白质-蛋白质相互作用和RNA折叠。分子动力学(MD)模拟一直是动态结构的主要来源。尽管已经提出了许多从MD数据中学习亚稳态的方法,但一些关键问题仍需要进一步研究。在此,我们简要回顾该领域的最新进展,重点关注属于两步聚类框架的一些流行方法,并希望吸引更多研究人员为该领域做出贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b941/8233889/544c66634720/ijms-22-06576-g001.jpg

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