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Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics
Shawn C C Hsueh1, Adekunle Aina1, Steven S Plotkin1,2
1Department of Physics and Astronomy, The University of British Columbia, Vancouver, BCV6T 1Z1, Canada.
The Journal of Physical Chemistry. B
|November 21, 2022
Summary
Reservoir replica exchange molecular dynamics (R-REMD) enhances cyclic peptide conformational sampling. This accelerated method efficiently captures thermodynamic properties and rare events, outperforming traditional replica exchange methods.
Area of Science:
- Computational chemistry
- Biophysics
- Molecular dynamics
Background:
- Cyclic peptide conformation dictates therapeutic efficacy.
- Existing methods struggle with entropic contributions and reliable thermodynamic properties.
- Accurate conformational ensembles are crucial for drug design.
Purpose of the Study:
- To implement and benchmark reservoir replica exchange molecular dynamics (R-REMD) for enhanced sampling of cyclic peptide conformations.
- To assess R-REMD's efficiency and accuracy compared to traditional replica exchange molecular dynamics (REMD).
- To evaluate R-REMD's capability in sampling rare events.
Main Methods:
- Implementation of R-REMD in GROMACS.
- Benchmarking on model systems: alanine dipeptide, Trpzip2, SARS-CoV-2 spike cyclic peptide, and oxytocin.
- Comparison of R-REMD with REMD for ensemble generation and thermodynamic property calculation.
Main Results:
- R-REMD demonstrated comparable results to REMD on Trpzip2 but on a significantly faster timescale.
- R-REMD showed marginally faster ensemble generation for cyclic peptides compared to REMD.
- R-REMD proved more effective in sampling rare events, including peptide bond isomerization.
Conclusions:
- R-REMD is a valid and efficient accelerated molecular dynamics method for generating reliable cyclic peptide conformational ensembles.
- R-REMD offers advantages in speed and sampling rare events over traditional REMD.
- The modified GROMACS source code for R-REMD is publicly available.
