Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

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.

Flap Dynamics in Aspartic Proteases: A Computational Perspective

“…Computational methods to understand conformational flexibility Computational methods have gradually become an important part of biochemistry and biophysics. To understand the free-energy surface, ligand binding, macroscopic and microscopic fluctuations in biomolecular systems, several methods have been developed (19,(22)(23)(24)(25)(26), some of which are listed in Table 2.…”

Use of Integrated Computational Approaches in the Search for New Therapeutic Agents

“…Biological systems often have many local energy minima separated by high‐energy barriers, and this can limit complete sampling as simulations can get stuck in local minima. Accordingly, a number of methods have been developed and applied that address the free energy sampling problem, such as, replica‐exchange molecular dynamics (using Monte Carlo weights to determine the probability of exchanging systems states), metadynamics,, and simulated annealing (SA), and an increasing number of new sampling algorithms is continuously emerging ,. The choice of the most suitable method depends on the biological and physical characteristics of the system under study.…”

“…Accordingly,anumber of methods have been developed and applied that address the free energy sampling problem, such as, replica-exchange molecular dynamics( using Monte Carlo weightst o determine the probability of exchanging systemss tates), [43] metadynamics [44,45] ,a nd simulateda nnealing (SA), [46] and an increasing number of new samplinga lgorithms is continuously emerging. [47,48] The choice of the most suitable method depends on the biological and physicalc haracteristics of the system under study.W hile conventional MD generates ac ontinuous trajectory at as ingle temperature, enabling kinetic properties to be directly extracted, SA -l ike many enhanced samplingm ethods-d oes not;h owever, SA is characterized bya ni mproved sampling efficiency and is well suited for very flexible systems. [49][50] In ordert oi mprove the overall quality of the simulations, hybrid approachesh ave also been developed.…”

Traveling without dwelling: Extending the timescale accessible to molecular dynamics simulation

“…Molecular dynamics (MD) simulations have been recognized as one of the most powerful tools for investigating dynamical processes at an atomistic level, such as impurity diffusion in a crystal and protein conformational changes. It is, however, well known that MD simulations often suffer from a severe limitation associated with the timescale [1][2][3][4]. With current computer resources, MD simulations using a standard protocol for sampling the canonical distribution cannot simulate dynamical processes taking place on a timescale of milliseconds or longer.…”