报告题目

从分子动力学模拟到蛋白的自由能谱解析

报告时间

2020年9月25日 15：00—16：00

报告地点

腾讯会议 ID：634252159

报告人

蒋杭进研究员浙江大学数据科学研究中心

报告人

简介

蒋杭进，2018年获香港中文大学统计学博士。同年入选浙江大学研究员，担任博士生导师。主要研究方向为分析来自不同领域的数据提出新的统计方法。在国际知名期刊Nature Astronomy, The Astrophysical Journal, Statistica Sinica, Methods等发表论文10余篇。

报告摘要

Deciphering the free energy landscape of   biomolecular structure space is crucial for understanding many complex   molecular processes, such as protein-protein interaction, RNA-folding, and   protein-folding. A major source of current dynamic structure data is   Molecular Dynamics (MD) simulations. Several methods have been proposed to   investigate the free energy landscape from MD data, but all of them rely on   the assumption that kinetic similarity is associated with global geometric   similarity, which may lead to unsatisfactory results. In this paper, we   proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical   free energy landscape by correlating local geometric similarity with kinetic   similarity. Its application on the benchmark alanine dipeptide MD data showed   a much better performance than existing methods in exploring and   understanding the free energy landscape. We also applied it to the MD data of   Villin HP35. Our results are more reasonable on various aspects than those   from other methods and very informative on the hierarchical structure of its   energy landscape.

邀请人

易鸣教授

2020年9月22日

学院

审核意见

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