Pietrucci, F. | bioRxiv Science

bioRxiv2025

Robust conformational space exploration of cyclic peptides by combining different MD protocols and force fields

Cyclic peptides are an important class of pharmaceutical drugs. We used here replica-exchange molecular dynamics (REMD) and simulated tempering (ST) simulations to explore the conformational landscape of a set of nine cyclic peptides. The N-ter to C-ter backbone cyclized peptides of seven to ten residues were previously designed for a high conformational stability with a mixture of L and D amino acids. Their experimental NMR structures were available in the protein data bank (PDB). For each peptide we tested several force fields, Amber96, Amber14, RSFF2C and Charmm36m in implicit and explicit solvent. We find that the variability of the free energy maps obtained from several protocols is larger than the variability obtained by just repeating the same protocol. Running multiple protocols is therefore important for the convergence assessment of REMD or ST simulations. The majority of the free energy maps showed clusters with a high RMSD to the native structures, revealing the residual flexibility of this set of cyclic peptides. The high RMSD clusters had in some cases the lowest free energy, rendering the prediction of the native structure more difficult with a single protocol. Fortunately, the combination of four implicit solvent REMD and ST simulations, mixing the Amber96 and Am-ber14 force fields, predicted robustly the native structure. As implicit solvent in the REMD or ST setup are up to one hundred times faster than explicit solvent simulations, so running four implicit solvent simulations is a good practical choice. We checked that the use of an explicit solvent REMD or ST simulation, taken alone or combined with implicit solvent simulations, did not significantly improved our results. It results that our combination of four implicit solvent simulations is tied in terms of success rate with much more expensive combinations that include explicit solvent simulations. This may be used as guideline for further studies of cyclic peptide conformations.

biophysics↗

bioRxiv2018

An enhanced-sampling MD-based protocol for molecular docking

Understanding molecular recognition of proteins by small molecules is key for drug design. Despite the number of experimental structures of ligand-protein complexes keeps growing, the number of available targets remains limited compared to the druggable genome, and structural diversity is generally low, which affects the chemical variance of putative lead compounds. From a computational perspective, molecular docking is widely used to mimic ligand-protein association in silico. Ensemble-docking approaches include flexibility through a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g. molecular dynamics. However, structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations (EDES - Ensemble-Docking with Enhanced-sampling of pocket Shape) to generate druggable conformations of proteins only exploiting their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to ligands to generate holo-like binding site geometries. We assessed the method on two challenging proteins undergoing different extents of conformational changes upon ligand binding. In both cases our protocol generated a significant fraction of structures featuring a low RMSD from the experimental holo conformation. Moreover, ensemble docking calculations using those conformations yielded native-like poses among the top ranked ones for both targets. This proof of concept study paves the route towards an automated workflow to generate druggable conformations of proteins, which should become a precious tool for structure-based drug design.

biophysics↗