Back

De novo Design of Peptides that Bind Specific Conformers of α-Synuclein

Wallace, H.; Yang, H.; Tan, S. K.; Pan, H.; Yang, R.; Xu, J.; Jo, H.; Condello, C.; Polizzi, N.; DeGrado, W.

2023-11-16 biophysics
10.1101/2023.11.14.567090 bioRxiv
Show abstract

Insoluble amyloids rich in cross-{beta} fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloid in a conformationally specific manner are lacking. We introduce a novel computational method for de novo design of peptides that tile the surface of -synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a "target backbone" for the design of a peptide binder. Next, we interrogate structures in the PDB database with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of -synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by -synuclein, tau, or A{beta}40. This designed peptide also markedly slowed the formation of -synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.