A coarse-grained force field for molecular dynamics simulations of native structures of proteins in a dissipative particle dynamics (DPD) framework is developed. The parameters for bonded interactions are derived by mapping the bonds and angles for 20 amino acids onto target distributions obtained from fully atomistic simulations in explicit solvent. A dual-basin potential is introduced for stabilizing backbone angles, to cover a wide spectrum of protein secondary structures. The backbone dihedral potential enables folding of the protein from an unfolded initial state to the folded native structure. The proposed force field is validated by evaluating structural properties of several model peptides and proteins including the SARS-CoV-2 fusion peptide, consisting of α-helices, β-sheets, loops and turns. Detailed comparisons with fully atomistic simulations are carried out to assess the ability of the proposed force field to stabilize the different secondary structures present in proteins. The compact conformations of the native states were evident from the radius of gyration as well as the high intensity peaks of the root mean square deviation histograms, which were found to lie below 0.4 nm. The Ramachandran-like energy landscape on the phase space of backbone angles (θ) and dihedrals (ϕ) effectively captured the conformational phase space of α-helices at ~(ϕ=50°, θ=90°) and β-strands at ~(ϕ=±180°, θ=90°-120°). Furthermore, the residue-residue native contacts are also well reproduced by the proposed DPD model. The applicability of the model to multidomain complexes is assessed using lysozyme as well as a large α helical bacterial pore-forming toxin, cytolysin A. Our studies illustrate that the proposed force field is generic, and can potentially be extended for efficient in-silico investigations of membrane bound polypeptides and proteins using DPD simulations.
Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields
