Despite being among the most efficient and precise catalysts, enzymes are sometimes unable to be adapted to specific applications due to our limited ability to rationally tune and tailor catalytic function, particularly when seeking increased activity, but also when modifying specificity and selectivity. Take for example, the redesign of ketol-acid reductoisomerase (KARI), whose activity on a native substrate is sometimes the rate-limiting step in proposed industrial isobutanol production pathways. While traditional structure-based computational enzyme redesign strategies would typically focus on the enzyme-bound ground state and transition state, we postulated that additionally treating the underlying dynamics of complete turnover events that connect and pass through both states could further elucidate the structural properties affecting catalysis and help identify mutations that lead to increased catalytic activity.<br/><br/>To examine KARI’s substrate turnover dynamics with atomistic detail, computational methods based on path sampling techniques were adapted and applied to gather hundreds of thousands of QM/MM simulations of attempted substrate turnover by KARI – both productive, reactive and unproductive, non-reactive attempts. From these data, machine learning models were constructed and used to identify specific structural features (e.g., interatomic distances, angles, and torsions) associated with successful, productive catalysis. Subsequently, mutations expected to stabilize reactive structures over non-reactive structures, guided by machine learning-identified features, were predicted with multistate protein design approaches. This procedure has so far designed eight enzyme variants with a significant improvement in calculated activity relative to wild type, with the best variant’s relative improvement approaching 800-fold. Collectively, these results suggest that the enzyme-substrate complex’s conformation and dynamics before an attempted reaction can affect substrate conversion, an understanding of which may bolster enzyme redesign for increased activity.

Turnover dynamics and machine learning guide redesigning KARI for increased catalytic activity

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.