(467a) Design of Peptide Nanofibrils Using Monte-Carlo Sampling and Coarse-Grained Simulations

Peptide self-assembly into amyloid fibrils has numerous applications in drug delivery and biomedical engineering. It is known that peptides can self-assemble into architectures like nanofibers, nanosheets, nanotubes, nanoparticles, but understanding of the relationship between amino acid sequence and structures of assemblies is limited. We seek to establish a workflow to design previously unknown amino acid sequences to produce peptides that assemble into specific desired structures.

In this talk, I will describe a Monte-Carlo-based peptide assembly design (PepAD) algorithm that allows us to identify self-assembling peptides that form unique supramolecular structures. The self-assembling tendencies of the peptide sequences are tested by using DMD/PRIME20 simulations, a molecular dynamics simulation technique that uses a coarse-grained but realistic protein model to predict amyloid formation. We use the above-described computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into “antiparallel β-sheets” and “parallel β-sheets”. The starting points for this work are the 7-mer peptide fragment Aβ (16-22) (sequence: KLVFFAE), which is associated with Alzheimer’s disease, and the fibril-forming segment of the yeast prion protein Sup35 (sequence: GNNQQNY). Our efforts facilitate the identification of β-sheet-based self-assembling peptides and contribute insights towards fundamental sequence-to-assembly relationships in intrinsically disordered proteins.