Abstract
Molecular Dynamics simulations are a powerful approach to study biomolecular conformational changes or protein–ligand, protein–protein, and protein–DNA/RNA interactions. Straightforward applications, however, are often hampered by incomplete sampling, since in a typical simulated trajectory the system will spend most of its time trapped by high energy barriers in restricted regions of the configuration space. Over the years, several techniques have been designed to overcome this problem and enhance space sampling. Here, we review a class of methods that rely on the idea of extending the set of dynamical variables of the system by adding extra ones associated to functions describing the process under study. In particular, we illustrate the Temperature Accelerated Molecular Dynamics (TAMD), Logarithmic Mean Force Dynamics (LogMFD), and Multiscale Enhanced Sampling (MSES) algorithms. We also discuss combinations with techniques for searching reaction paths. We show the advantages presented by this approach and how it allows to quickly sample important regions of the free-energy landscape via automatic exploration.
Highlights
Molecular dynamics (MD) simulations have become a fundamental tool to study biological systems at atomic scale (Perilla et al, 2015)
Rather than reconstructing large portions of the free-energy space, one is interested in directly finding paths connecting different minima in this landscape. This different perspective was first introduced by Pratt (1986) using a “chain-of-states” linking two minima, and successively developed by Chandler and collaborators in the transition path sampling (TPS) technique (Bolhuis et al, 2002), where the basic idea is to reconstruct the ensemble of transition paths directly from pieces of dynamical trajectories joining the two minima
The common feature of these methods is that they rely on extending the phase space of the physical system under study by adding a set of extra variables considered as dynamical ones, linked to the original system via collective variables (CVs) functions of the physical coordinates
Summary
Molecular dynamics (MD) simulations have become a fundamental tool to study biological systems at atomic scale (Perilla et al, 2015). The problem originates from the presence of dynamical hindrances of energetic or entropic nature, which confine the system in specific regions of phase space Transitions among those metastable states, while being rare events on the simulation timescale, are often a mandatory requirement for biological function. From its statistical mechanics definition, the free energy is related to the probability density of a set of collective variables (CVs), which are typically used to study a reactive event. These are functions of the Cartesian variables of the original system, such as distances and angles between atoms, or more complicated functions. We examine techniques designed to explore and reconstruct free-energy surfaces, while afterwards we illustrate methods devised to determine optimal reaction pathways between metastable states
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