Machine Learning Identifies Chemical Characteristics That Promote Enzyme Catalysis.

ABSTRACT: Despite tremendous progress in understanding and engineering enzymes, knowledge of how enzyme structures and their dynamics induce observed catalytic properties is incomplete, and capabilities to engineer enzymes fall far short of industrial needs. Here, we investigate the structural and dynamic drivers of enzyme catalysis for the rate-limiting step of the industrially important enzyme ketol-acid reductoisomerase (KARI) and identify a region of the conformational space of the bound enzyme-substrate complex that, when populated, leads to large increases in reactivity. We apply computational statistical mechanical methods that implement transition interface sampling to simulate the kinetics of the reaction and combine this with machine learning techniques from artificial intelligence to sele

SUBMITTER: Bonk BM 

PROVIDER: S-EPMC6407039 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature