A New Multi-scale Platform For Investigating Peptide Self- Assembly

Summary: A new systematic multi-scale coarse-graining procedure is applied to optimize a coarse-grained model of polyalanine to reproduce the properties of a reference atomistic simulation. Peptide aggregation plays a role in a number of neurodegenerative diseases, such as Alzheimer's disease, Huntington’s disease, and Parkinson’s disease. Here we aim to develop an accurate molecular-scale picture of this process using a multi-scale computational approach. Recently, Shell developed a coarse-graining methodology that is based on a thermodynamic quantity called the relative entropy [1], a measure of how different two molecular ensembles behave. By minimizing the relative entropy between a coarse-grained peptide system and a reference allatom system, with respect to the coarse-grained model's parameters, an optimized coarse-grain model can be obtained. Here we develop a corresponding numerical strategy for optimizing coarse-grained peptide models. We first verify this approach by running a test optimization where known potential parameters are recovered successfully. Subsequently, we use the algorithm to generate a coarse-grained model of a polyalanine peptide 15 residues long (ALA 15 ) using an atomistic simulation as a reference. a) b)