Efficient equilibrium sampling of all-atom peptides using library-based Monte Carlo

TL;DR

This paper demonstrates that library-based Monte Carlo (LBMC) significantly accelerates equilibrium sampling of all-atom peptides compared to Langevin dynamics, using pre-calculated fragment libraries and two solvent models.

Contribution

The study extends LBMC to all-atom peptide sampling with implicit solvents, showing over 100-fold efficiency improvements over traditional methods.

Findings

LBMC is over 100 times faster than Langevin dynamics.

LBMC effectively samples peptides with different solvent models.

Pre-calculated fragment libraries enable rapid equilibrium sampling.

Abstract

We applied our previously developed library-based Monte Carlo (LBMC) to equilibrium sampling of several implicitly solvated all-atom peptides. LBMC can perform equilibrium sampling of molecules using the pre-calculated statistical libraries of molecular-fragment configurations and energies. For this study, we employed residue-based fragments distributed according to the Boltzmann factor of the OPLS-AA forcefield describing the individual fragments. Two solvent models were employed: a simple uniform dielectric and the Generalized Born/Surface Area (GBSA) model. The efficiency of LBMC was compared to standard Langevin dynamics (LD) using three different statistical tools. The statistical analyses indicate that LBMC is more than 100 times faster than LD not only for the simple solvent model but also for GBSA.