Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics

TL;DR

This paper introduces an adaptive reinforced dynamics (RiD) method with clustering and tuning techniques, enabling efficient exploration of high-dimensional free energy landscapes in molecules and proteins, outperforming existing methods.

Contribution

The work presents a novel RiD scheme with adaptive clustering and tuning, capable of handling many CVs and high barriers, demonstrated on complex molecular systems.

Findings

Successfully constructed a 9D free energy landscape of peptoid trimer.

Observed folding/unfolding rates of chignolin as 4.30 μs^{-1}.

Achieved a 14.6 GDT-HA score improvement in protein structure refinement.

Abstract

Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time, they have long faced a number of issues such as the inefficiency when dealing with a large number of collective variables (CVs) or systems with high free energy barriers. In this work, we show that with \redc{the clustering and adaptive tuning techniques}, the reinforced dynamics (RiD) scheme can be used to efficiently explore the configuration space and free energy landscapes with a large number of CVs or systems with high free energy barriers. We illustrate this by studying various representative and challenging examples. Firstly we demonstrate the efficiency of adaptive RiD compared with other methods, and construct the 9-dimensional free energy landscape of peptoid trimer which has energy barriers of more than 8 kcal/mol. We then study the folding of the protein chignolin using 18 CVs. In this case, both the folding and unfolding rates are observed to be equal to 4.30~$\mu s^{-1}$. Finally, we propose a protein structure refinement protocol based on RiD. This protocol allows us to efficiently employ more than 100 CVs for exploring the landscape of protein structures and it gives rise to an overall improvement of 14.6 units over the initial Global Distance Test-High Accuracy (GDT-HA) score.