(557b) Towards a Predictive Coarse-Grained Model for Computational Design of Bioadhesives

Development of biocompatible adhesives and sealants has sparked enormous research interest in sea animals such as barnacles and mussels that are able to utilize a collection of flexible unstructured proteins to maintain strong and lasting adhesion with wet surfaces. A good understanding of the surface forces unique for these proteins provides an avenue to designing biomimetic adhesives that can work well in aqueous environments. Because a thorough exploration of the parameter space is experimentally difficult, bioadhesive design will benefit greatly from theoretical insights into the essential physics and a faithful description of the key ingredients that can be tailored for specific applications. Atomistic methods are not feasible because of the large system size and the extensive parameter space affiliated with the polypeptide sequence and the local solution environment (e.g. pH, salt types and ionic strength). In this work, we present a coarse-grained model for amino acids that is computationally efficient and captures the important forces underlying surface-protein interactions. Classical density functional theory (CDFT) provides a robust and efficient computational tool to describe the surface adhesion energy as well as charge regulation and the microscopic structure of polypeptides at various aqueous-solid interfaces. The coarse-grained model is able to account for the volume exclusion effects, electrostatic interactions, and hydrophobic attractions in good agreement with experiment. It predicts the activity coefficients of bulk amino-acid solutions and their adsorptions to various inorganic surfaces semi-quantitatively. The coarse-grained model allows us to investigate the bioadhesive behavior of polypeptides under various environmental conditions. The theoretical results can be directly compared with known experimental data for biomimetic surface forces.