A Structure-Based Platform for Predicting Chemical-Induced Parkinson's Disease

Current methods for determining the neurotoxic potential of (agro)chemicals are not comprehensive enough, as is suggested by the increased incidence of Parkinson's disease (PD) among people exposed to certain pesticides. Mechanism-based in silico screening can address this shortcoming by predicting molecular initiating event activation, a precursor of the adverse outcome. However, a limited amount of protein-binding data has been collected on pesticides, meaning that for screening, an approach is required that is well suited for extrapolation to a wide variety of chemicals. Here, the group I metabotropic glutamate receptors (mGluRs) were taken as a case study because of their role in chemical-induced PD. Compounds with known activity for these receptors were docked into the allosteric binding site, interaction fingerprints (IFPs) were computed, and used to train classification models. Afterward, model enrichment was evaluated, feature importance was derived, and the applicability domain was analyzed. Both IFP-based mGluR models demonstrated good enrichment with the area under the Receiver Operating Characteristic Curve (ROC AUC) being 0.78 and 0.66. The interactions that were most important for model predictions were hydrogen bond formation with Asn760 for mGluR1 and aromatic interactions with Trp785 for mGluR5. The applicability domain varied depending on the training set but was consistently larger for IFPs than for Morgan fingerprints, a fragment-based descriptor. A virtual screen using the final models identified 132 potential mGluR binders, of which one, bifenthrin, had been previously found to bind in in vitro experiments. To promote the implementation of the screening technique presented here, a platform was created where users can make predictions using our models. All in all, these results highlight the potential of combining IFPs with machine learning and contribute to the shift toward mechanism-based in silico toxicology.