Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

Abstract

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.