Zhongyue (John) Yang
I am an Assistant Professor of Chemistry and also affiliated to the Data Science Institute. My group and I are actively exploring tremendous opportunities in the interface between computational chemical biology and data science. Born in Tianjin, China, I graduated from Nankai University with a B.S. degree in Chemistry (Po-Ling class) in 2013. I received my Ph.D. with Prof. K. N. Houk in the Department of Chemistry at the University of California, Los Angeles in 2017. During my graduate career, we developed computational tools to simulate single-molecule reaction trajectories in solvent and in enzyme, which enables the elucidation of time-resolved mechanisms of biomolecular transformations in the condensed environment. From 2018 to 2020, I was a postdoctoral scholar in the group of Prof. Heather J. Kulik in the Department of Chemical Engineering at Massachusetts Institute of Technology. During this time, we leveraged large-scale quantum mechanics computation and molecular dynamics simulations to quantify constrained-peptide switch, understand the catalytic origins of methyltransferases, and predict the catalytic actions of glycyl radical enzymes in human gut microbiota.
My research group is broadly interested in developing and applying computational platforms via integration of first-principles simulations and data-driven models to quantify, understand, and design biomolecules that are critical to human health and bioengineering. Biomolecules serve as the building blocks of all life processes. The insights into their interactions and chemical transformations are essential for developing new drugs, biocatalysts, and therapeutic strategies. Enabled by the rapid evolution of computing power and data science algorithms, simulating the structure, dynamics, and chemical functions of biomolecules are now faster and more accurate than ever before. This grants us a special opportunity to innovate the high-throughput computational protocols to facilitate and guide bimolecular design and discovery for catalytic and medicinal uses.
Specifically, our research missions involve:
1) Develop new first-principles simulation protocols to rationally design protein mutants for enhancing the rate or selectivity for enzyme catalysis and peptide machine.
2) Develop new machine learning models to efficiently predict protein mutational hotspots for functional enhancement.
3) Develop an automatic high-throughput computational workflow to design biomolecules for catalytic, pharmaceutical, energy, and environmental applications.