A Coarse-grained Minimal Model for the Hierarchical Self-assembly of Biocompatible Optoelectronic Nanostructures

Self-assembling peptides containing aromatic groups are attractive targets for bioelectronic materials design due to their ease of manufacture, biocompatibility, aqueous solubility, and controllability of side chain chemistry. Microscopic understanding of the properties that control assembly is a prerequisite for rational design. In this work, we employ a patchy particle model to efficiently traverse the parameter space of interactions. We study the effects of side chain and aromatic group interaction strength and side chain steric constraints on peptide assembly kinetics and thermodynamics. We characterize the growth rate of different cluster types and the fractal dimension of the final aggregates to identify parameters that lead to rapid growth of linear aggregates with interacting aromatic cores. Using these parameters, we identify sequence-defined candidate peptides for high-resolution computational and experimental testing. Our work leads to greater understanding of the parameters controlling aggregation and demonstrates a method for identification of candidate chemistries to assemble hierarchical nanostructures with desirable optoelectronic properties.