Jiang Hangjin, Fan Xiaodan
Center for Data Science, Zhejiang University, Hangzhou 310058, China.
Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China.
Int J Mol Sci. 2021 Jun 19;22(12):6576. doi: 10.3390/ijms22126576.
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.
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。