A transferable explicit-solvent polarizable coarse-grained model for proteins

The application of classical molecular dynamics (MD) simulations at atomic resolution (fine-grained level - FG), to the majority of biomolecular processes, remains limited because of the associated computational complexity of representing all the atoms. This problem is magnified in presence of protein-based biomolecular systems that have a very large conformational space and MD simulations with fine-grained resolution have slow dynamics to explore this space. Current transferrable coarse-grained (CG) force fields in literature are either limited to only peptides with the environment encoded in an implicit form (cannot study environmental heterogeneity) or cannot capture transitions into secondary/tertiary peptide structures from a primary sequence of amino acids. In this work, we present a transferrable CG forcefield with an explicit representation of the environment for accurate simulations with proteins. The forcefield consists of a set of pseudo-atoms representing different chemical groups, that can be joined/associated together to create different biomolecular systems. This preserves the transferability of the forcefield to multiple environments and simulation conditions. We have added electronic polarization that can respond to environmental heterogeneity/fluctuations and couple it to protein’s structural transitions. The non-bonded interactions are derived by fitting the parameters to reproduce experimentally observed and simulation-derived free energies. The bonded interactions are parametrized from the distributions of the particular bonded terms in a non-redundant peptide structure database. We also present validations of the CG model with simulations of well-studied systems - TRP-cage, TrpZip4, GB1, EF-hand (a common calcium binding motif), Huntingtin-N17, and Glycophorin A in a diverse environmental setting - presence of lipid interfaces, ions and extracellular matrix. The CG model can aid biomolecular research, particularly in a realistic cell-like crowded heterogeneous environment.