Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning
Robert I. Hornea, Ewa A. Andrzejewskaa, Parvez Alamb, Z. Faidon Brotzakisa, Ankit Srivastavab, Alice Auberta, Magdalena Nowinskaa, Rebecca C. Gregorya, Roxine Staatsa, Andrea Possentia, Sean Chiaa c, Pietro Sormannia, Bernardino Francesco Ghettid, Byron W. Caugheyb, Tuomas P. J. Knowlesa, Michele Vendruscoloa e✉
- aCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- bLaboratory of Neurological Infections and Immunity, Rocky Mountain Laboratories, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
- cBioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- dDepartment of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- eCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK. mv245@cam.ac.uk
- ✉Corresponding author
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Abstract
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.