Multiplexed-Replica Exchange Molecular Dynamics Method for Protein Folding Simulation

Abstract

Simulating protein folding thermodynamics starting purely from a protein sequence is a grand challenge of computational biology. Here, we present an algorithm to calculate a canonical distribution from molecular dynamics simulation of protein folding. This algorithm is based on the replica exchange method where the kinetic trapping problem is overcome by exchanging noninteracting replicas simulated at different temperatures. Our algorithm uses multiplexed-replicas with a number of independent molecular dynamics runs at each temperature. Exchanges of configurations between these multiplexed-replicas are also tried, rendering the algorithm applicable to large-scale distributed computing (i.e., highly heterogeneous parallel computers with processors having different computational power). We demonstrate the enhanced sampling of this algorithm by simulating the folding thermodynamics of a 23 amino acid miniprotein. We show that better convergence is achieved compared to constant temperature molecular dynamics simulation, with an efficient scaling to large number of computer processors. Indeed, this enhanced sampling results in (to our knowledge) the first example of a replica exchange algorithm that samples a folded structure starting from a completely unfolded state.

Prediction of Protein Loop Conformations Using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

“…49,59 The nonpolar free energy is written as 44 (7) where the first term is the cavity term, γ i is the surface tension proportionality constant for atom i, and A i is the solvent exposed surface area of atom i. The second term is the dispersion interaction term which is given by 44 (8) where α i is an adjustable solute-solvent van der Waals dispersion parameter for atom i.…”

Serial Replica Exchange

“…One in particular is the multiplexed replica exchange method. 31 In contrast to that method, the SREM does not require synchronization between the different walkers (processors), leaves no CPUs idle, and thus has substantially lower overhead. Another method suitable for distributed environments is simulated tempering, 32 which is inherently a serial algorithm.…”

Protein folding: Then and now

“…In addition, world-wide parallel computing (e.g. Folding@Home [31]) and generalized ensemble sampling techniques that involve parallel simulations of molecular systems coupled with a Monte Carlo (MC) protocol [32,33] have been successfully applied to protein folding [25,[34][35][36].…”