Prof. Yihan Shao
Title: Physics and Machine Learning Based Methods for Modeling the Enzyme Reactions and Bioimaging Probes
Bio:
Dr. Shao studied Chemical Graph Theory and Chemical Group Theory at Nanjing University, where he obtained his B.Sc. degree in 1993. He then developed linear-scaling density functional theory methods for electronic ground states and spin-flip density functional theory for electronic excited states as a Ph.D. student at the Department of Chemistry at the University of California at Berkeley and graduated in 2002. He spent a dozen years further developing electronic structure code at Q-Chem Inc, a Quantum Chemistry software company. He returned to academia in 2016 as an Assistant Professor at the University of Oklahoma and was promoted to Associate Professor in 2022. He moved his group to Brandeis University in August 2025. Dr. Shao’s research in Computational Chemistry and Biology has been funded by NIH, NSF, DOE, and other agencies, which has led to over 170 peer-reviewed publications.
Abstract:
In this seminar, we will present novel methods from our lab for modeling enzymatic reactions and bioimaging probes. To fully understand an enzyme reaction, it is essential for us to identify its minimum free energy pathway. To reduce the steep computational cost of these free energy simulations, we have adapted the multiple time-step integration algorithm and machine learning potentials in these simulations. We will showcase the power of our simulation protocols with chorismate mutase and CRISPR-Cas9 enzymes. For bioimaging probes, we will show how to analyze the interactions between chromophore orbitals and substituent (or solvent) orbitals, and explain how electron-donating and withdrawing groups could modulate the chromophore emission wavelengths.
Keywords: Enzyme catalysis, bioimaging, computational modeling, machine learning
Faculty Host: Prof. Yang Yang