Abstract
Molecular dynamics (MD) simulations are a powerful tool to follow the time evolution of biomolecular motions in atomistic resolution. However, the high computational demand of these simulations limits the timescales of motions that can be observed. To resolve this issue, so called enhanced sampling techniques are developed, which extend conventional MD algorithms to speed up the simulation process. Here, we focus on techniques that apply global biasing functions. We provide a broad overview of established enhanced sampling methods and promising new advances. As the ultimate goal is to retrieve unbiased information from biased ensembles, we also discuss benefits and limitations of common reweighting schemes. In addition to concisely summarizing critical assumptions and implications, we highlight the general application opportunities as well as uncertainties of global enhanced sampling.
Highlights
Molecular dynamics (MD) simulations are a powerful tool to follow the time evolution of biomolecular motions in atomistic resolution
Exchanges between neighboring replicas are attempted at defined time intervals and accepted or rejected based on an energetic criterion, which retains the canonical distribution with bk = 1/kBTk, bl = 1/kBTl being the inverse of the temperature Tk and Tl multiplied by the Boltzmann constant kB
This potential limitation is balanced against the advantageous features of the method, i.e. that the convergence behavior of Integrated temperature sampling (ITS) has been observed to be superior to other global enhanced sampling techniques and that it is computationally substantially more efficient than the related temperature replica exchange MD (T-REMD) in terms of CPU time.[45]
Summary
Anna Sophia Kamenik received her doctoral degree in Chemistry from the University of Innsbruck, Austria (UIBK) under the supervision Prof. Trivial.[23] Substantial research efforts are currently invested in the optimization and automatization of selecting appropriate CVs, e.g. with the aid of machine learning.[24,25,26] Given relevant CVs, pathway-dependent methodologies can perform strikingly well, for example in modelling the activation of voltage-sensing domains of ion channels,[27,28] the estimation of ligand koff rates,[29] or membrane permeation probability calculations.[30,31] Despite these successes, for many interesting biomolecular systems it is not straight-forward to derive a small number of representative observables as CVs. For example, when we simulate cyclic peptides in apolar environments, we usually observe one (or a few) well-defined ‘‘closed’’ structures. This process, typically referred to as reweighting, is in practice often decisive for the applicability of biasing methods
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