A new multiscale algorithm and its application to coarse-grained peptide models for self-assembly

Abstract

Peptide self-assembly plays a role in a number of diseases, in pharmaceutical degradation, and in emerging biomaterials. Here, we aim to develop an accurate molecular-scale picture of this process using a multiscale computational approach. Recently, Shell (Shell, M. S. J. Chem. Phys. 2008, 129, 144108-7) developed a coarse-graining methodology that is based on a thermodynamic quantity called the relative entropy, a measure of how different two molecular ensembles behave. By minimizing the relative entropy between a coarse-grained peptide system and a reference all-atom system, with respect to the coarse-grained model's force field parameters, an optimized coarse-grained model can be obtained. We have reformulated this methodology using a trajectory-reweighting and perturbation strategy that enables complex coarse-grained models with at least hundreds of parameters to be optimized efficiently. This new algorithm allows for complex peptide systems to be coarse-grained into much simpler models that nonetheless recapitulate many correct features of detailed all-atom ones. In particular, we present results for a polyalanine case study, with attention to both individual peptide folding and large-scale fibril assembly.