Coarse kMC-based replica exchange algorithms for the accelerated simulation of protein folding in explicit solvent

Abstract

In this paper, we present a coarse replica exchange molecular dynamics (REMD) approach, based on kinetic Monte Carlo (kMC). The new development significantly can reduce the amount of replicas and the computational cost needed to enhance sampling in protein simulations. We introduce 2 different methods which primarily differ in the exchange scheme between the parallel ensembles. We apply this approach on folding of 2 different β-stranded peptides: the C-terminal β-hairpin fragment of GB1 and TrpZip4. Additionally, we use the new simulation technique to study the folding of TrpCage, a small fast folding α-helical peptide. Subsequently, we apply the new methodology on conformation changes in signaling of the light-oxygen voltage (LOV) sensitive domain from Avena sativa (AsLOV2). Our results agree well with data reported in the literature. In simulations of dialanine, we compare the statistical sampling of the 2 techniques with conventional REMD and analyze their performance. The new techniques can reduce the computational cost of REMD significantly and can be used in enhanced sampling simulations of biomolecules.

Characterizing the Interplay between Polymer Solvation and Conformation

“…Aqueous or organic solutions of conformationally flexible solutes, such as polymers or peptides, play an important role in diverse materials − and biomolecular contexts. − Although solvated flexible molecules can exist in a multitude of conformations, they often display two or more distinct basins that are separated by free energy barriers, e.g., polymers can be in extended or collapsed states, whereas proteins can be in their native folded structures, become denatured, or even adopt well-defined misfolded configurations. − Molecular simulations have been extensively used, often with the aid of enhanced sampling methods, to characterize the conformational free energy landscapes of diverse flexible molecules, typically as a function of solute coordinates, such as radius of gyration or dihedral angles. , …”

Machine-Learning Enabled New Insights into the Coil-to-Globule Transition of Thermosensitive Polymers Using a Coarse-Grained Model

“…This enables us to reach the time- and length-scales of several tens of microseconds (μs) and tens of nanometers (nm), respectively. − Various methods such as force-matching, the iterative Boltzmann approach, etc. have been utilized to develop CG models of these macromolecules and solvents. ,− Recently, we have developed a novel approach that integrates an artificial neural network (ANN) based machine-learning model and particle swarm optimization (PSO) algorithm with MD simulations to accelerate the development of CG models (ANN-assisted PSO method) . Despite all these advancements, developing CG models of any stimuli-sensitive polymers that can accurately capture their conformational transitions has been a grand challenge.…”

Hierarchical parallelization of divide‐and‐conquer density functional tight‐binding molecular dynamics and metadynamics simulations

“…The structural evolution of the alanine peptide in water is a good test case as it can be used to verify RE‐based simulation techniques. [ 34,100–111 ]…”