Aß Oligomer Dissociation Is Catalyzed by Fibril Surfaces.

Oligomeric assemblies consisting of only a few protein subunits are key species in the cytotoxicity of neurodegenerative disorders, such as Alzheimer's and Parkinson's diseases. Their lifetime in solution and abundance, governed by the balance of their sources and sinks, are thus important determinants of disease. While significant advances have been made in elucidating the processes that govern oligomer production, the mechanisms behind their dissociation are still poorly understood. Here, we use chemical kinetic modeling to determine the fate of oligomers formed in vitro and discuss the implications for their abundance in vivo. We discover that oligomeric species formed predominantly on fibril surfaces, a broad class which includes the bulk of oligomers formed by the key Alzheimer's disease-associated Aß peptides, also dissociate overwhelmingly on fibril surfaces, not in solution as had previously been assumed. We monitor this "secondary nucleation in reverse" by measuring the dissociation of Aß42 oligomers in the presence and absence of fibrils via two distinct experimental methods. Our findings imply that drugs that bind fibril surfaces to inhibit oligomer formation may also inhibit their dissociation, with important implications for rational design of therapeutic strategies for Alzheimer's and other amyloid diseases.

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

Design of amyloidogenic peptide traps.

Segments of proteins with high ß-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in ß-strand and ß-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid ß1-42 (Aß42). The Aß binders block the assembly of Aß fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aß42 species.