Methods for the establishment of enzymatic mechanisms – from QM to ML
TL;DR
Recent developments in ML potentials, ML collective variables, and committor-based sampling are presented as innovative methods that have been able to address some of the current challenges in accuracy, sampling efficiency, and the identification of low-dimensional representations of reaction coordinates.
Abstract
We offer a practical and conceptual introduction to some of the current approaches to modelling enzymatic reaction mechanisms, ranging from quantum mechanics (QM), molecular mechanics (MM), and hybrid QM/MM approaches to enhanced sampling methods, knowledge-based approaches, and machine learning (ML) advances. We discuss how static and dynamic QM/MM approaches, as well as multi-PES strategies, have contributed to understanding the role of conformational diversity, electrostatic preorganization and solvent participation in the determination of catalytic barriers and reaction paths. We focus on how advanced sampling techniques and data-driven collective variables have enabled the exploration of rare events and reaction coordinates, as well as how knowledge- and rule-based approaches have facilitated the interpretation and hypothesis generation for different families of enzymes. Recent developments in ML potentials, ML collective variables, and committor-based sampling are presented as innovative methods that have been able to address some of the current challenges in accuracy, sampling efficiency, and the identification of low-dimensional representations of reaction coordinates. A case study of α-amylase demonstrates how the combination of these strategies leads to a comprehensive understanding of enzyme reactivity, from the chemical to the conformational level. Collectively, these developments contribute to a predictive understanding of enzymatic catalysis, which will have extensive implications in enzyme engineering, sustainable chemistry, and drug discovery. Advances in high performance computing, automated simulation pipelines and data formats will likely make multiscale simulation more accessible and reproducible. Simultaneously, the combined application of mechanistic knowledge, ML, and experimental validation will hopefully advance the discovery and optimization of biocatalysts with well-defined properties, tailored to meet pressing societal needs, such as plastic biodegradation, carbon sequestration, sustainable synthesis and personalised medicine.