Multicomponent and Supramolecular Self-Assemblies as Functional Biomaterials

There is a pressing need to develop bioactive matrices that promote cellular interactions and elicit desirable regenerative behavior in vivo. This is particularly important in the context of ischemic stroke where a focal lesion forms forestalling the regrowth of brain tissue. We can develop and synthesize these matrices utilizing peptide-based molecules as building blocks to create supramolecular structures that emulate the properties of the native healthy extracellular matrix (ECM) within the central nervous system (CNS). In order to facilitate the regeneration of lost and/or damaged tissue, we propose using peptidic biomaterials that have the ability to emulate the properties of the native healthy extracellular matrix (ECM) within the CNS. The work completed in this thesis focuses on employing a combinatorial strategy involving computational modeling and experimental approaches to design and synthesize stimuli-responsive, self-assembling biomaterial systems that mimic many of the biochemical and mechanical properties, such as the viscoelastic properties, bioactive motifs, etc. found in the ECM. Additionally, we leveraged the power of atomistic molecular dynamics simulations to examine the dynamical effects of systematically perturbing the pentapeptide sequence motif. This enables us to screen for a myriad of design candidates in silico, and promising leads that exhibit higher order self-assembling behavior will later be experimentally produced.