Toward understanding whole enzymatic reaction cycles using multi-scale molecular simulations

Area of Science:

• Biochemistry
• Computational Chemistry
• Molecular Biology

Background:

• Enzymes catalyze reactions via substrate binding, chemical transformations, and product release.
• Enzyme conformational changes often accompany these catalytic events.
• Theoretical analysis is complex due to coupled events and conformational dynamics.

Purpose of the Study:

• To summarize recent multiscale molecular simulations of enzyme functions.
• To highlight the integration of multiple microscopic events in enzyme catalysis.
• To discuss the role of machine learning in simulating enzyme catalysis.

Main Methods:

• Advanced molecular simulations, including molecular dynamics (MD).
• Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations.
• Multiscale simulations incorporating multiple microscopic events.
• Machine learning (ML) methods for describing microscopic events.

Main Results:

• Multiscale simulations can predict the direction of enzymatic reaction cycles.
• Accurate free energy changes are crucial for predicting reaction cycles.
• ML methods achieve quantum chemistry accuracy in describing catalytic events.
• ML/MM simulations offer new developments for studying enzyme catalysis.

Conclusions:

• Coupling enzyme conformational changes with chemical reactions is key to understanding catalytic cycles.
• Machine learning significantly enhances the accuracy and scope of enzyme catalysis simulations.
• Advanced computational methods provide unprecedented insights into enzyme mechanisms.