A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site

TL;DR

This paper introduces a multi-resolution simulation approach coupling atomistic and coarse-grained models to efficiently and accurately study enzyme systems, capturing both local binding details and global protein fluctuations.

Contribution

A novel methodology for coupling atomistic and coarse-grained models in enzyme simulations, enabling flexible resolution choices without sacrificing accuracy.

Findings

Successfully models stable ligand binding.

Provides computational speedup over traditional methods.

Identifies key degrees of freedom in enzymatic function.

Abstract

In multi-resolution simulations, different system components are simultaneously modelled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of substrate binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modelled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically, without loss of accuracy in the atomistic description. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity via an exploration of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function.