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

Chemistry

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

Z. Song, H. Zhou, et al.

Discover how Zilin Song, Hongyu Zhou, Hao Tian, Xinlei Wang, and Peng Tao leverage advanced hybrid quantum mechanical molecular mechanical techniques to unravel the intricate mechanisms of bacterial β-lactamases and their role in antibiotic resistance. This research unveils predictive energy models and rate-limiting steps, offering insights that align with empirical studies.

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Playback language: English
Abstract
Bacterial β-lactamases, enzymes contributing to antibiotic resistance, are studied using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled via re-optimization under various protein environments from constrained molecular dynamics. Machine learning regression creates predictive potential energy surface models. Two model-independent criteria quantify energetic contributions and correlations, identifying rate-limiting steps. The workflow's consistency is tested across various quantum chemistry theories and machine-learning models, showing qualitative agreement with experimental mutagenesis studies. The TEM-1/benzylpenicillin acylation reaction serves as a model system.
Publisher
Communications Chemistry
Published On
Oct 08, 2020
Authors
Zilin Song, Hongyu Zhou, Hao Tian, Xinlei Wang, Peng Tao
Tags
bacterial β-lactamases
antibiotic resistance
quantum mechanical methods
machine learning
energy pathways
TEM-1
benzylpenicillin
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