Computing reaction rates in bio-molecular systems using discrete macro-states

TL;DR

This paper reviews various computational methods for estimating reaction rates in biomolecular systems, focusing on conformational changes and categorizing approaches like reactive flux, transition path sampling, and conformation dynamics.

Contribution

It provides a structured overview of existing methods for computing biomolecular reaction rates, highlighting their chronological development and combining features.

Findings

Organized methods into four main categories.

Compared approaches like reactive flux, transition path sampling, and conformation dynamics.

Discussed hybrid methods such as non-equilibrium umbrella sampling and weighted ensemble dynamics.

Abstract

Computing reaction rates in biomolecular systems is a common goal of molecular dynamics simulations. The reactions considered often involve conformational changes in the molecule, either changes in the structure of a protein or the relative position of two molecules, for example when modeling the binding of a protein and ligand. Here we will consider the general problem of computing the rate of transfer from a subset A of the conformational space Omega to a subset B of Omega. It is assumed that A and B are associated with minimum energy basins and are long-lived states. Rates can be obtained using many different methods. We review some of the most popular approaches. We organize the different approaches roughly in chronological order and under four main categories: reactive flux, transition path sampling, conformation dynamics. The fourth class of methods, to which we do not give any specific name, in some sense attempts to combine features from transition path sampling and conformation dynamics. They include non-equilibrium umbrella sampling (Warmflash et al. [2007], Dickson et al. [2009b]), and weighted ensemble dynamics (Huber and Kim [1996]).