Self-assembly of Phenylalanine Oligopeptides: Development of Transferable Bottom-up Coarse-grained Potentials

Abstract

Self-assembled nanostructures comprising Phenylalanine-based oligopeptides are widely utilized in various applications across the domains of electronics, personal care, and biomedicine. Numerous factors, including sequence, determine the morphology of these nanostructures. Computational approaches such as bottom-up coarse-grained (CG) models coupled with the Molecular Dynamics simulation technique can be used to predict morphologies as a function of the sequence. These approaches efficiently resolve the multiple scales relevant to the self-assembly process while generating CG representations that are consistent with the all-atom representations. However, bottom-up CG models are computationally demanding to develop. This study proposes an approach to develop bottom-up CG models of Phenylalanine oligopeptides that are transferable between sequences to resolve their assembly. The approach builds upon a previously reported bottom-up CG model for the Phenylalanine tripeptide to develop parameters for new degrees of freedom associated with other oligopeptides. The performance of the new CG model is analyzed by comparing simulation results with corresponding results obtained using different versions of the Martini force field. The predictions from the new force field are also compared with available experimental results.