Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2.

The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organisms. In this study, we developed novel strategies for multi-species screening of estrogen receptor (ER) agonists in plastic additives using AlphaFold2. Firstly, Deep Forest (DF), artificial neural network (ANN) and conventional machine learning (ML) models were utilized to screen ERα agonists. The DF models using RDKit.Chem.Descriptors and MorganFingerprint achieved a sensitivity = 0.96, specificity > 0.99, and an F1 score > 0.95, identifying 42 plastic additives as ERα agonists. Subsequently, ERα structures for Danio rerio (Dr), Oryzias melastigma (Om), Delphinus delphis (Dd), Physeter catodon (Pc), Mytilus edulis (Me), Xenopus tropicalis (Xt), Nipponia nippon (Nn), and Aptenodytes forsteri (Af) were constructed using AlphaFold2. Except for Me ERα, most species shared two common key amino acid residues responsible for ERα activity: arginine 85 and glutamic acid 44 (aligned serial numbers in the LBD). However, aquatic-related species exhibited other three additional key residues: glycine 212, leucine 216 and phenylalanine 95 (aligned serial numbers in the LBD). The number of compounds with docking energy < -9 kcal/mol for Dr, Om, Dd, Pc, Me, Xt, Nn, and Af were 4, 8, 4, 12, 10, 13, 7, and 9, respectively. The docking energy of estrone in all species was < -9 kcal/mol, while that of bisphenol P varied greatly among different species. The combined application of ML and AlphaFold enables high-throughput evaluation of the ecotoxicity posed by emerging pollutants across multiple aquatic-related species.