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 quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

“…The high degrees of freedom of protein molecules present challenges also referred to as the curse of dimensionality. To face this challenge, various dimensionality reduction methods have been applied to MD simulations under the assumption that a few degrees of freedom through coordinate projections could account for the majority of the protein functions. − The projections obtained can then be used as collective variables (CVs) to build a Markov state model (MSM). MSMs have been applied to identify protein functional states on the free-energy surface and to describe the transitions among them. − …”

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways

“…In summary, on all reactive pathways ( Figure 4 c–f), the protonation of the β-lactam nitrogen (P2 and P3) is deemed to be the highest energy contribution, in agreement with our previous investigations on TEM-1/benzylpenicillin acylation. 85 …”

“…Interestingly, in the Toho/AMP: R2-AE pathways, the highest individual contribution comes from P2 (the proton transfer between Lys73 and Ser130), while in Toho/CEX: R2-AE, it was determined as P3 (the protonation of the cephem nitrogen). In summary, on all reactive pathways (Figure c–f), the protonation of the β-lactam nitrogen (P2 and P3) is deemed to be the highest energy contribution, in agreement with our previous investigations on TEM-1/benzylpenicillin acylation …”

Mimicking Enzymes: The Quest for Powerful Catalysts from Simple Molecules to Nanozymes

“…Iterative cycles of mutagenesis and screening or selection are applied to modify existing protein properties to enhance their catalytic activities or develop completely new catalysts for non-natural chemical transformations . The availability of sophisticated computational methods such as machine learning will possibly constitute one of the prominent approaches in the future for the engineering of new enzymes or enzyme mimics. − …”