| Popis: |
Harnessing the self-assembly of peptide sequences has demonstrated great promise in the domain of creating high precision shape-tunable biological materials. The unique properties of peptides allow for a building blocks approach to materials design. In this proposal, self-assembly of mixed systems encompassing two peptide sequences with identical hydrophobic regions and distinct polar segments is investigated. The two peptide sequences are diphenylalanine and phenylalanine-asparagine-phenylalanine. The dissertation aims to examine the impact of molecular composition (i.e, the total peptide concentration and the relative tripeptide concentration) on the morphology of the self-assembled hybrid biological material. We report a rich polymorphism in the assemblies of these peptides and explain the relationship between peptide sequence, concentration and the morphology of the supramolecular assembly. We discuss three techniques to explore the phase space of this morphological diversity: Classical Coarse Grained Molecular Dynamics (CGMD), Replica Exchange Molecular Dynamics (REMD), and a hybrid Workflows/Machine Learning framework for Targeted Design of Supramolecular Assemblies, which we have called PACE: Pipelines for Automation of Compliance-based Elimination. Classical CGMD establishes the polymorphism in the phase space, REMD sheds light on the thermodynamics of assembly and helps determine the most likely thermodynamic states, and the PACE framework provides a workflow to automate the simulation, detection and screening of configurations that can target specific morphologies. |
