报告名称：Investigating conformational changes of biological macromolecules using Markov State Models

报 告 人：Dr. Xuhui Huang

报告时间：2012年9月6日（星期四）上午10:00

报告地点：嘉定园区学术活动中心三楼报告厅

报告简介：

Simulating biologically relevant timescales at atomic resolution is a challenging task since typical atomistic simulations are at least two orders of magnitude shorter. Markov State Models (MSMs) built from molecular dynamics (MD) simulations provide one means of overcoming this gap without sacrificing atomic resolution by extracting long time dynamics from short MD simulations. MSMs coarse grain space by dividing conformational space into long-lived, or metastable, states. This is equivalent to coarse graining time by integrating out fast motions within metastable states. By varying the degree of coarse graining one can vary the resolution of an MSM; therefore, MSMs are inherently multi-resolution. In this talk, I will demonstrate the power of MSMs by applying it to simulate the complex conformational changes that occurs at tens of microsecond timescales for a large RNA transcription complex (close to half million atoms). In the second part of my talk, I will introduce a new efficient dynamic clustering algorithm for the automatic construction of MSMs for multi-body systems. We have successfully applied this new algorithm to model the protein-ligand recognition and hydrophobic collapse processes that occur at a mixture of different timescales.

报告人简介：

EDUCATION

07/2006, Ph.D in Chemical Physics, Chemistry Dept, Columbia University

06/2001, Bachelor of Science, University of Science and Technology of China

ACADEMIC POSITIONS

01/2010-Present, Assistant Professor, Chemistry Dept, the Hong Kong University of Science and Technology

09/2008-12/2009, Research Associate, Bioengineering Dept, Stanford University

08/2006-09/2008, Postdoctoral Scholar, Bioengineering Dept, Stanford University

ACADEMIC AWARDS

CCG Excellence Award, American Chemical Society, COMP Division, 2006

RESEARCH INTEREST

My recent research has been focused on understanding thermodynamics and kinetics of the protein folding and ligand binding using molecular simulations. I have developed various novel computational algorithms to enhance conformational sampling.