From Sequence to Catalytic Activity
We have established a connection between evolutionary sequence data and enzyme activity/stability (PNAS, 2022) using generative AI. This prompts us to introduce the concept of ‘evolutionary catalysis (NSR, 2023).’ Our research provides a fresh perspective on enzyme catalysis and evolution, marking the beginning of an exciting journey. We are committed to advancing this field.
Rational Enzyme Engineering
We have leveraged naturally evolved enzyme sequences to enhance activity and stability beyond their wild-type counterparts, which have been optimized over billions of years (PNAS, 2023). Our work also demonstrated that sequence data can predict individual steps in enzymatic reactions (JACS, 2025). Currently, our lab is involved in enzyme engineering across diverse areas, such as natural product biosynthesis. Additionally, we are conducting bioinformatics analysis and genome mining to discover novel enzymes of interest.
AI-Driven Drug Discovery
Our focus lies in employing AI and computational chemistry for the purpose of designing drugs that target enzymes (JACS, 2022). Specifically, we concentrate on achieving drug selectivity. This necessitates the utilization of AI for molecular representation and generation, alongside computational chemistry for assessing drug selectivity.