Shi Wen-Jun, Cao Zhou, Long Xiao-Bing, Yao Chong-Rui, Zhang Jin-Ge, Chen Chang-Er, Ying Guang-Guo
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China.
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China.
J Hazard Mater. 2025 Aug 15;494:138629. doi: 10.1016/j.jhazmat.2025.138629. Epub 2025 May 14.
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.
有效的公共数据库的缺失极大地限制了对水生生物中核受体介导的激素效应的高通量预测。在本研究中，我们利用AlphaFold2开发了用于在塑料添加剂中多物种筛选雌激素受体（ER）激动剂的新策略。首先，利用深度森林（DF）、人工神经网络（ANN）和传统机器学习（ML）模型筛选ERα激动剂。使用RDKit.Chem.Descriptors和MorganFingerprint的DF模型实现了灵敏度=0.96、特异性>0.99以及F1分数>0.95，鉴定出42种塑料添加剂为ERα激动剂。随后，使用AlphaFold2构建了斑马鱼（Dr）、黑背纹胸鮡（Om）、瓶鼻海豚（Dd）、抹香鲸（Pc）、紫贻贝（Me）、热带爪蟾（Xt）、朱鹮（Nn）和帝企鹅（Af）的ERα结构。除了Me ERα外，大多数物种共享两个负责ERα活性的常见关键氨基酸残基：精氨酸85和谷氨酸44（在LBD中的比对序列号）。然而，与水生相关的物种还表现出另外三个关键残基：甘氨酸212、亮氨酸216和苯丙氨酸95（在LBD中的比对序列号）。对于Dr、Om、Dd、Pc、Me、Xt、Nn和Af，对接能量< -9 kcal/mol的化合物数量分别为4、8、4、12、10、13、7和9。雌酮在所有物种中的对接能量均< -9 kcal/mol，而双酚P在不同物种间的对接能量差异很大。ML和AlphaFold的联合应用能够对多种与水生相关的物种中新兴污染物造成的生态毒性进行高通量评估。
