Bottom-up Coarse-grained Molecular Simulations of Peptoids with Enhanced Sampling

Peptoids are a class of synthetic polymers that have similar biocompatibility but high stability compared to peptides. Peptoids can both self-assemble into highly ordered nanostructures and direct the organization of inorganic components. Computational modeling is promising in helping discover and design peptoid-based nanomaterials. Atomistic peptoid models have been developed but only consider small systems due to high computational costs. To reach the time and length scale of peptoid assembly, we develop a coarse-grained (CG) model reparametrized from all-atom (AA) simulations by fitting CG bonded interactions through iterative Boltzmann inversion and nonbonded interactions through potential of mean force matching. We use Parallel Bias metadynamics to obtain good sampling in the cis/trans isomerization. The proposed CG model demonstrates excellent agreement with AA distribution functions and opens a new avenue to the computational inverse design of peptoid-based nanomaterials.