Since the 3D structures and the functionalities of proteins are determined by their primary amino acid sequences, peptide- and protein-based self-assemblies offer cost-effective routes to advanced functional biomaterials. Computational protein sequence design was performed for peptides in the context of various assembly nanostructures using a statistical mechanical approach to identify sequences and structures. Molecular dynamics simulations were used to understand the assemblies’ molecular properties. Monte Carlo simulated annealing was utilized to explore the size and the geometry of the peptide building blocks. The probabilities of the amino acids for the targeted nanostructures were calculated. Using a similar structural motif, the designed peptides spontaneously assemble into soluble coiled-coil bundles and crystalline networks. Experimental studies confirm assembly properties of the peptides, including molecular orientation, spacing, and lattice symmetries. Molecular dynamics simulations were to explore structural fluctuations within model assemblies as well as to investigate how changes in solution conditions affect the assemblies. This work demonstrates the promising potential to apply computational methods in guiding the <i>de novo</i> design of novel self-assembling peptides and proteins towards the discovery of advanced biomaterials.

Computational design of peptide-based self-assemblies

251st National Meeting (2016)

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