Talk title: Interrogating proteins as molecular machines aided by machine learning

Speaker: Peng Tao, PhD
Associate Professor
Southern Methodist University

Abstract: 

Protein molecules are in constant motion yet perform their functions with remarkable specificity and accuracy. Computational modeling and simulation, alongside experimental studies, provide critical insights into the structure, dynamics, and function of proteins. Recently, machine learning has emerged as a powerful tool to advance biomolecular modeling and simulations. Our research applies these techniques to investigate two fundamental aspects of protein dynamics and function. 1) One area of focus is allosteric proteins, which undergo shape and activity changes in response to molecule binding at allosteric sites, transmitting structural changes across the protein to regulate function through subtle conformational shifts. Using molecular dynamics (MD) simulations, we have developed computational methods to capture the underlying mechanisms of protein allostery at an atomic level, providing a deeper understanding of this dynamic regulation. 2) Additionally, we explore enzyme catalysis, where enzymes serve as molecular machines driving essential biochemical reactions. Traditionally studied with quantum mechanical/molecular mechanical (QM/MM) methods, our work now uses machine learning to investigate diverse enzyme-catalyst reaction landscapes. Recent efforts include machine learning models that predict acylation mechanisms for β-lactamase TEM-1 with benzylpenicillin and employ explainable AI (XAI) to gain mechanistic insights into Toho-1 β-lactamases with substrates like ampicillin and cefalexin. By integrating protein dynamics, these models uncover relationships between enzyme dynamics, catalytic activity, and evolutionary traits through MD simulations. These two research avenues converge in our goal to create a unified framework that characterizes proteins as dynamic molecular machines, offering new insights into their functional mechanisms.

Speaker Bio:

Dr. Peng Tao is an Associate Professor in the Department of Chemistry at Southern Methodist University (SMU), where he has been a faculty member since 2013. He earned his B.S. in Chemistry (1998) and M.S. in Physical Chemistry (2001) from Peking University, China, followed by a Ph.D. in Computational Chemistry from Ohio State University in 2007. Dr. Tao’s research focuses on developing computational methods to explore protein allostery and the evolution of protein catalytic mechanisms. He also applies machine learning techniques to enhance the depth and efficiency of computational tools in studying biomolecules as molecular machines. His work has attracted multiple federal research grants, including an NSF CAREER award to create a machine learning-based theoretical framework for enzyme catalytic mechanism evolution. Dr. Tao is a recipient of the American Chemical Society OpenEye Junior Faculty Award and the Ralph E. Powe Junior Faculty Enhancement Award. He has authored 78 peer-reviewed publications.

Host:
Lauren McCormick, PhD