Gaussian-Accelerated Molecular Dynamics with the Weighted Ensemble Method: A Hybrid Method Improves Thermodynamic and Kinetic Sampling.

Surl-Hee Ahn,Anupam A Ojha,Rommie E Amaro,J Andrew Mccammon

doi:10.1021/acs.jctc.1c00770

Surl-Hee Ahn, Anupam A Ojha + Show 2 more

Open Access

https://doi.org/10.1021/acs.jctc.1c00770

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Abstract

Gaussian-accelerated molecular dynamics (GaMD) is a well-established enhanced sampling method for molecular dynamics simulations that effectively samples the potential energy landscape of the system by adding a boost potential, which smoothens the surface and lowers the energy barriers between states. GaMD is unable to give time-dependent properties such as kinetics directly. On the other hand, the weighted ensemble (WE) method can efficiently sample transitions between states with its many weighted trajectories, which directly yield rates and pathways. However, convergence to equilibrium conditions remains a challenge for the WE method. Hence, we have developed a hybrid method that combines the two methods, wherein GaMD is first used to sample the potential energy landscape of the system and WE is subsequently used to further sample the potential energy landscape and kinetic properties of interest. We show that the hybrid method can sample both thermodynamic and kinetic properties more accurately and quickly compared to using either method alone.

Highlights

Molecular dynamics (MD) simulations are becoming quintessential tools in many fields, including biology, chemistry, materials science, chemical and biological engineering, and medicine
We show how Gaussian accelerated molecular dynamics (GaMD)-weighted ensemble (WE) outperforms either method in obtaining thermodynamic and kinetic properties
To further illustrate that GaMD surpasses the WE method in getting the free energy landscape, we show the free energy landscapes of bovine pancreatic trypsin inhibitor (BPTI) in explicit solvent obtained from the two methods

Summary

Introduction

Molecular dynamics (MD) simulations are becoming quintessential tools in many fields, including biology, chemistry, materials science, chemical and biological engineering, and medicine. An increasing number of researchers have used MD simulations to uncover mechanisms of their biological system of interest in atomistic detail. Applications of MD simulations range from studying protein folding, protein-protein or protein-ligand interactions to computer-aided drug design (virtual screening and ligand docking). MD simulations are not without their challenges. MD simulations have to be run using femtosecond time steps due to being limited by the fastest motions in the system (e.g., bond-length vibrations). Biological processes of interest are often on the order of microseconds or longer. MD simulations can be computationally costly when attempting to observe rare events, which is often the case of interest

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