Markov-Chain Monte Carlo Methods for Simulations of Biomolecules

TL;DR

This paper reviews Markov Chain Monte Carlo methods, focusing on advanced techniques like generalized ensembles and biased updating, which are crucial for biomolecular simulations and have potential to improve sampling efficiency.

Contribution

It provides a comprehensive review of MCMC techniques, emphasizing generalized ensembles and biased updating methods relevant for biomolecular simulation advancements.

Findings

Discussion of multicanonical ensemble and replica exchange methods

Analysis of statistical techniques for MCMC simulations

Potential applications in biomolecular research

Abstract

The computer revolution has been driven by a sustained increase of computational speed of approximately one order of magnitude (a factor of ten) every five years since about 1950. In natural sciences this has led to a continuous increase of the importance of computer simulations. Major enabling techniques are Markov Chain Monte Carlo (MCMC) and Molecular Dynamics (MD) simulations. This article deals with the MCMC approach. First basic simulation techniques, as well as methods for their statistical analysis are reviewed. Afterwards the focus is on generalized ensembles and biased updating, two advanced techniques, which are of relevance for simulations of biomolecules, or are expected to become relevant with that respect. In particular we consider the multicanonical ensemble and the replica exchange method (also known as parallel tempering or method of multiple Markov chains).