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bioRxiv · 10.1101/2023.11.09.565678

Design of parallel ????-sheet nanofibrils using Monte-Carlo search, coarse-grained simulations, and experimental testing

Abstract

Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously-established computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into "parallel {beta}-sheets", i.e., {beta}-sheets with N-terminus-to-C-terminus {beta}-strand vectors oriented in parallel. To accomplish the desired {beta}-strand organization, we applied the PepAD amino acid sequence design software to the Class-1 cross-{beta} spine defined by Sawaya et al. This molecular configuration includes two layers of parallel {beta}-sheets stacked such that N-terminus-to-C-terminus vectors are oriented antiparallel for molecules on adjacent {beta}-sheets. The first cohort of PepAD identified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7-mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril-like structures with a predominantly parallel {beta}-sheet arrangement, two formed fibril-like structure with <50% in parallel {beta}-sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallel {beta}-sheets based on Fourier Transform Infrared Spectroscopy, Circular Dichroism, Electron Microscopy, and Thioflavin-T fluorescence spectroscopy measurements.

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BibTeXRIS

Sarma, S., Sudarshan, T. R., Nguyen, V., Robang, A. S., Xiao, X., Le, J. V., Helmicki, M. E., Paravastu, A. K., Hall, C. K.. 2023-11-13. Design of parallel ????-sheet nanofibrils using Monte-Carlo search, coarse-grained simulations, and experimental testing. https://doi.org/10.1101/2023.11.09.565678

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