Professor Xuhui Huang

Professor Xuhui Huang
Department of Chemistry
University of Wisconsin-Madison
Abstract

Non-Markovian Dynamic Models for Studying Protein Conformational Changes

Protein’s dynamic transitions between metastable conformational states play an important role in numerous biological processes. Markov State Models (MSMs) provide a powerful approach to study these dynamic processes by predicting long time scale dynamics based on many short molecular dynamics (MD) simulations. In the first part of this talk, I will introduce our group’s work on developing the MSM methodology and applying it to simulate the complex conformational changes, that occurs millisecond timescales for RNA Polymerase complexes containing about half a million atoms (e.g., backtracking). MSMs provide a useful approach to study protein conformational changes, but it is challenging to build truly Markovian models due to the limited length of lag time (bound by the length of relatively short MD simulations). In the second part of my talk, I will introduce our recent work on developing non-Markovian dynamic models based on the Generalized Master Equation (GME) theory that encodes the dynamics in a generally time-dependent memory kernel, whose characteristic decay time scale corresponds to the kernel lifetime. We show that GME methods can greatly improves upon Markovian models by accurately predicting long timescale dynamics using much shorter MD trajectories on complex conformational changes.

I will also introduce our Integrative GME (IGME) based on the time integrated memory kernels to avoid huge numerical instability in the memory kernel tensor. Finally, I will present our newly developed deep- learning approach, the Memory Kernel Minimization based Neural Networks (MEMnets), which can accurately identify the slow CVs of biomolecular dynamics. MEMnets is built on the GME theory. Its key innovation is the development of a novel loss function that corresponds to the integrals of memory kernels in encoder deep-neural networks. We show that MEMnets can successfully elucidate the gate opening dynamics of a bacterial RNA polymerase, a process occurring at millisecond timescale. We expect that the GME-based methods hold promise to be widely applied to study functional dynamics of proteins.

Xuhui Huang

Professor Xuhui Huang obtained his Ph.D. from Columbia University in 2006 with Prof. Bruce Berne. He did his postdoc research at Stanford University with Profs. Michael Levitt and Vijay Pande. He was as an Assistant, Associate and Full Professor of the Hong Kong University of Science and Technology (HKUST) between 2010 and Summer 2021. 

Since Fall 2021, he took up the position of the Hirschfelder Endowed Chair Professor in Theoretical Chemistry, and Director of Theoretical Chemistry Institute at University of Wisconsin- Madison. He has received numerous awards, including Biophysical Society Theory & Computation Award for Mid- Career Scientists (2023), Pople Medal from the Asia-Pacific Association of Theoretical and Computational Chemists (2021), American Chemical Society OpenEye Outstanding Junior Faculty Award (2014), and Hong Kong Research Grant Council Early Career Award (2013). He is a founding member of Young Academy of Sciences of Hong Kong (YASHK) and Fellow of Royal Society of Chemistry (FRSC). His group pioneered in elucidating the dynamics of protein conformational changes by developing new methods based on statistical mechanics that can bridge the gap between experiments and atomistic MD simulations.

Hosted by Professor Jiali Gao