Computational studies of peptide self-assembly

Research into novel biological materials for use in biomedical applications is guided by the formation of supramolecular structures which have properties resultant from the characteristics of the compositional molecules. Peptides are commonly utilized in biological material development as their properties are widely variable and highly controllable due to the sequence-specific properties of amino acids. Self-assembling peptides are of specific interest due to their spontaneous aggregation into organized morphologies with predictable characteristics based upon their constituent amino acids. Though novel peptide materials have traditionally been researched through physical experimentation, the development of Molecular Dynamics has allowed for comparable computational studies of peptide systems. In this work, coarse-grained Molecular Dynamics simulations are selected to study self-assembling peptides from two classes, aromatic and aliphatic, as these peptides have been experimentally validated to spontaneously assemble themselves into nanostructures. Computational models representative of the peptides’ chemistry are created for aromatic peptides FF (phenylalanine-phenylalanine) and FNF (phenylalanine-asparagine-phenylalanine) and aliphatic peptides A6K (alanine-alanine-alanine-alanine-alanine-alanine-lysine), V6K (valine-valine-valine-valine-valine-valine-lysine), and V6K2 (valine-valine-valine-valine-valine-valine-lysine-lysine). In the aromatic studies, the effect of varying total peptide concentrations and relative tripeptide concentrations on the morphology of the assembled structures is characterized. In the aliphatic studies, the peptide alignment in stable aggregates and nanostructures is determined. The results demonstrate the viability of these peptide systems to form stable, usable nanostructures suitable for inclusion in biological applications that require the respective specific properties of the in-scope peptides.M.S.Includes bibliographical reference