The aggregation of α-synuclein is a central event in Parkinsons’s disease and related synucleinopathies. Since pharmacologically targeting this process, however, has not yet resulted in approved disease-modifying treatments, there is an unmet need of developing novel methods of drug discovery. In this context, the use of chemical kinetics has recently enabled accurate quantifications of the microscopic steps leading to the proliferation of protein misfolded oligomers. As these species are highly neurotoxic, effective therapeutic strategies may be aimed at reducing their numbers. Here, we exploit this quantitative approach to develop a screening strategy that uses the reactive flux toward α-synuclein oligomers as a selection parameter. Using this approach, we evaluate the efficacy of a library of flavone derivatives, identifying apigenin as a compound that simultaneously delays and reduces the formation of α-synuclein oligomers. These results demonstrate a compound selection strategy based on the inhibition of the formation of α-synuclein oligomers, which may be key in identifying small molecules in drug discovery pipelines for diseases associated with α-synuclein aggregation.

Significant efforts have been devoted in the last twenty years to developing compounds that can interfere with the aggregation pathways of proteins related to misfolding disorders, including Alzheimer’s and Parkinson’s diseases. However, no disease-modifying drug has become available for clinical use to date for these conditions. One of the main reasons for this failure is the incomplete knowledge of the molecular mechanisms underlying the process by which small molecules interact with protein aggregates and interfere with their aggregation pathways. Here, we leverage the single molecule morphological and chemical sensitivity of infrared nanospectroscopy to provide the first direct measurement of the structure and interaction between single Aβ42 oligomeric and fibrillar species and an aggregation inhibitor, bexarotene, which is able to prevent Aβ42 aggregation in vitro and reverses its neurotoxicity in cell and animal models of Alzheimer’s disease. Our results demonstrate that the carboxyl group of this compound interacts with Aβ42 aggregates through a single hydrogen bond. These results establish infrared nanospectroscopy as a powerful tool in structure-based drug discovery for protein misfolding diseases.

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson’s disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.

Drug development is an increasingly active area of application of machine learning methods, due to the need to overcome the high attrition rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases where very few disease-modifying drugs have been approved. To address this problem, we describe a machine learning approach to identify specific inhibitors of the proliferation of α-synuclein aggregates through secondary nucleation, a process that has been implicated in Parkinson’s disease and related synucleinopathies. We use a combination of docking simulations followed by machine learning to first identify initial hit compounds and then explore the chemical space around these compounds. Our results demonstrate that this approach leads to the identification of novel chemical matter with an improved hit rate and potency over conventional similarity search approaches.

The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson’s disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of α-synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.