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Probability Density Reweighting of High-Temperature Molecular Dynamics.

Jia-Nan ChenBotao DaiYun-Dong Wu
Published in: Journal of chemical theory and computation (2024)
Molecular dynamics (MD) simulation is a popular method for elucidating the structures and functions of biomolecules. However, exploring the conformational space, especially for large systems with slow transitions, often requires enhanced sampling methods. Although conducting MD at high temperatures provides a straightforward approach, resulting conformational ensembles diverge significantly from those at low temperatures. To address this discrepancy, we propose a novel probability density-based reweighting (PDR) method. PDR exhibits robust performance across four distinct systems, including a miniprotein, a cyclic peptide, a protein loop, and a protein-peptide complex. It accurately restores the conformational distributions at high temperatures to those at low temperatures. Additionally, we apply PDR to reweight previously studied high-T MD simulations of 12 protein-peptide complexes, enabling a comprehensive investigation of the conformational space of protein-peptide complexes.
Keyphrases
  • molecular dynamics
  • density functional theory
  • protein protein
  • binding protein
  • high temperature
  • high resolution
  • mass spectrometry
  • small molecule
  • virtual reality
  • monte carlo