(342ad) Computational Studies of the Phase Transitions and Self-Assembly of Thermoresponsive Peptide-Based Biomaterials

Peptide based biomaterials have garnered significant attention recently due to their ability to respond to external stimuli such as heat, light, and pH, thus allowing for controllable release of therapeutics for drug delivery applications and tunable mechanical and chemical properties for tissue engineering and in vitro cell culture applications. While experimental synthesis and characterization of biomaterials can be time consuming and limited in terms of resolution, simulations allow for efficient screening of broad design spaces while also giving insight into the molecular mechanisms and driving forces governing the complex phase behavior and assembly of responsive biomaterials. For simulations to produce experimentally relevant predictive design rules, there is a need to develop molecular models that can capture the thermodynamics and self-assembly of biopolymers at large enough length scales and time scales which are applicable to experiments. In this poster I will present my doctoral thesis work using computational tools, such as atomistic and coarse-grained molecular dynamics (MD) simulations, and machine learning to study responsive, peptide-based materials, such as elastin-like peptides (ELP), collagen-like peptides (CLP), and ELP-CLP bioconjugates. I will highlight our recent studies on the lower critical solution temperature (LCST)-like phase behavior of ELPs and ELP-CLP conjugates, the melting transitions of CLPs with natural and non-natural amino acids, and the self-assembly of CLPs into triple helices, fibrils, and supramolecular networks. Furthermore, I will discuss our recent efforts in using feedforward neural networks to predict supramolecular assembly and aggregate sizes of CLPs with the goal of designing peptides which display multiscale assembly. Overall, this work highlights the benefits of using a suite of computational techniques, including MD simulations and artificial neural networks, to streamline the discovery of self-assembling biopolymers with tunable physicochemical properties.