Enhanced Sampling Algorithms

TL;DR

This paper reviews enhanced sampling algorithms like multicanonical, simulated tempering, and replica-exchange methods, which improve biomolecular simulations by overcoming energy minima trapping, and discusses their extensions and effectiveness in peptide and protein systems.

Contribution

It provides a comprehensive review of generalized-ensemble algorithms and their extensions, highlighting their application and effectiveness in biomolecular simulations.

Findings

Effective in sampling diverse conformations of peptides and proteins.

Extensions improve algorithm efficiency and applicability.

Single simulation runs can yield multiple thermodynamic quantities.

Abstract

In biomolecular systems (especially all-atom models) with many degrees of freedom such as proteins and nucleic acids, there exist an astronomically large number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. Enhanced conformational sampling techniques are thus in great demand. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present various extensions of these three generalized-ensemble algorithms. The effectiveness of the methods is tested with short peptide and protein systems.