De Novo Design of Peptides that Co-assemble into β-sheet Based Nanofibrils Dataset
Author(s)
Xiao, Xingqing
Wang, Yiming
Seroski, Dillon T.
Wong, Kong M.
Liu, Renjie
Hudalla, Gregory A.
Hall, Carol K.
Advisor(s)
Editor(s)
Collections
Supplementary to:
Abstract
Peptides’ hierarchical co-assembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a novel computational and experimental approach to design pairs of charge-complementary peptides that selectively co-assemble into β-sheet nanofibers when mixed together, but remain unassembled when isolated separately. The key advance is a pep_tide _c_o-_a_ssembly _d_esign (PepCAD) algorithm that searches for pairs of co-assembling peptides. Six peptide pairs are identified from a pool of ~106 candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively co-assemble in biophysical experiments, with four forming β-sheet nanofibers, and one forming a stable non-fibrillar aggregate. Solid-state NMR, which is applied to characterize the co-assembling pairs, suggests that the _in-silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/-) peptides.
Sponsor
National Science Foundation Division of Chemical, Bioengineering, Environmental and Transport Systems Grant 1743432; National Science Foundation Division of Advanced Cyberinfrastructure Grant 1548562
Date
2021
Extent
Resource Type
Dataset
Resource Subtype
Rights Statement
Creative Commons Attribution 4.0 International