Computational modeling is extensively used to understand mechanisms and catalytic origins of enzyme catalysis. They hold great promise to predict the impact of mutations on enzyme efficiency, or even design new enzymes. In most cases, molecular modeling depends on manually-based quantum mechanics (QM) cluster models and molecular dynamics (MD) workflows. The demand of manual work makes it impossible to identify beneficial enzyme mutations in a high-throughput fashion. In this talk, I will discuss our ongoing development of a new high-throughput (HTP) enzyme modeling software enabling easy, high-throughput generation of molecular modeling outcomes derived from QM cluster, quantum mechanics/molecular mechanics (QM/MM), and MD simulations. This tool will enable researchers to efficiently study the mechanic pictures behind the complex enzymatic processes and to discover new engineering principles to identify function-enhancing enzyme variants.

EnzyHTP: A high-throughput platform for enzyme modeling

ACS Spring 2022

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