Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach.

ABSTRACT: The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively qua

SUBMITTER: Song Z 

PROVIDER: S-EPMC9814854 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature