Date/Time
Date(s) - 02/24/2025
3:00 pm - 4:00 pm
Location
Communicore, C1-004
Enzymes are remarkable biocatalysts that have evolved over billions of years and remain a central focus of biochemical research. Despite significant advances, accurately predicting enzyme catalytic activity using either AI or computational chemistry remains a major challenge. Recently, we demonstrated that generative AI-based sequence analysis can effectively predict the effects of mutations on enzyme activity. Our approach successfully identified beneficial mutations that significantly enhanced enzyme activity, achieving a high success rate in experimental studies. Furthermore, we explored the sequence-activity relationship through molecular simulations, revealing how nature preorganizes active center architecture to optimize catalysis. These simulations provided physics-based features that hold promise for advancing rational enzyme engineering. Overall, our study integrates generative AI-driven sequence analysis with molecular simulations, offering a framework for advancing enzymology and guiding future enzyme engineering efforts.
Bio
Wenjun Xie obtained his B.S. in Chemistry and Statistics and Ph.D. in Physical Chemistry from Peking University, China. He then completed postdoctoral training at MIT and USC before joining the Department of Medicinal Chemistry at the University of Florida. His research lab focuses on enzyme catalysis and engineering, as well as reaction-based drug design.