We use kinetic simulations of reaction dynamics to investigate the structural and dynamic drivers of enzyme catalysis for the rate-limiting step of the industrially important enzyme ketol-acid reductoisomerase (KARI), and we 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 select features relevant to reactivity and to build predictive models for reactive trajectories. We find that conformational descriptors alone, without the need for dynamic ones, are sufficient to predict reactivity with greater than 85% accuracy (90% AUC). Key descriptors distinguishing reactive from almost-reactive trajectories quantify substrate conformation, substrate bond polarization, and metal coordination geometry and suggest their role in promoting substrate reactivity. Quantum mechanical analysis and information theoretic metrics are used to study the pathways by which conformational drivers induce electronic structural effects that promote the reaction mechanism. Additionally, we find that trajectories constrained to visit a region of the reactant well, separated from the rest by a simple hyperplane defined by ten conformational parameters, show increases in computed reactivity by many orders of magnitude. This study provides evidence for the existence of reactivity promoting regions within the conformational space of the enzyme−substrate complex and develops methodology for identifying and validating these particularly reactive regions of phase space. Furthermore, the work demonstrates how mechanistic insights can be gleaned from structural drivers. We suggest that identification of reactivity promoting regions and re-engineering enzymes to preferentially populate them may lead to significant rate enhancements.

Machine learning identifies chemical drivers of enzyme catalysis

2019 Fall National Meeting

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