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

Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.

Section: Resultssupporting

confidence: 91%

Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations

Kasavajhala

Simmerling

2023

“…We described cyclic peptide conformations using the backbone dihedral angles (ϕ, ψ) for each residue, which are typically represented in the continuous range between −180° and 180°. In order to further simplify the structure definitions, the (ϕ, ψ) space can be separated into discrete regions, and each region can be assigned a “structural digit” (denoted by Greek letters here). ,, For example, in our previous work on cyclic pentapeptides, we used the (ϕ, ψ) distribution of cyclo-(GGGGG) to guide the discretization of the (ϕ, ψ) space for cyclic pentapeptides (Figure A,B) . First, the (ϕ, ψ) distribution of cyclo-(GGGGG) was binned into a 100 × 100 grid, and the probability density of each grid point was calculated.…”

Section: Methodsmentioning

confidence: 99%

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles

Hui

Descoteaux

Miao

et al. 2023

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R 2 = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R 2 = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R 2 = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R 2 = 0.97 and R 2 = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

“…REMD in explicit solvent is well established, and peptide folding in explicit solvent using the reservoir method was recently reported. 22 Finally, extending the protocol to incorporate constant pH MD methods would permit changes to receptor protonation states for different poses, 51 particularly with histidine side chains that are present in many binding pockets such as those studied here.…”

Section: ■ Conclusionmentioning

confidence: 99%

“…In this work, we present a proof-of-concept study that the reservoir REMD (res-REMD) approach that we developed , and applied − to generating conformational ensembles for peptides, proteins, and RNA hairpins could also be used to accelerate the sampling of alternate ligand poses without waiting for rare binding/unbinding events. The reservoir approach also has been used for accelerating convergence of constant pH and other types of simulations. − By applying this method here to pose sampling, flexibility in the ligand and receptor can be included; these have been shown to improve VS poses during MD rescoring . Local sampling is enhanced by a modest 4-temperature REMD ladder.…”

Section: Introductionmentioning

confidence: 99%

Rapid Rescoring and Refinement of Ligand–Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir

Alcantara,

Chiu,

Bickel

et al. 2023

Virtual screening (VS) involves generation of poses for a library of ligands and ranking using simplified energy functions and limited flexibility. Top-scored poses are used to rank and prioritize ligands. Here, we adapt the reservoir replica exchange molecular dynamics (res-REMD) method to rerank poses generated through VS. REMD simulations are carried out but with occasional Monte Carlo jumps to alternate VS-generated poses using a Metropolis criterion. The simulations converge within 10 ns for all systems, generating populations of alternate poses in the context of fully flexible ligand and protein side chains. The protocol is applied to four model protein–ligand complexes, where DOCK resulted in two successes and two scoring failures. In all four systems, the most populated cluster from the final ensemble exhibits high similarity to the crystallographic pose with ligand RMSD values under 2.0 Å. Both DOCK failures were rescued. For one DOCK success, the protocol identified the correct pose but also sampled an alternate pose at equal probability. Opportunities for future improvements and extensions are discussed.