A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant–Human Protein Interactions

Air pollution poses a critical global public health challenge. Molecular-level initiating events, such as pollutant-protein interactions, can trigger cascades of biological responses that may contribute to adverse health effects. However, current methods are limited in their ability to systematically identify these early binding events, particularly for emerging airborne pollutants, which hinders mechanistic understanding and risk assessment of pollution-related toxicity. To address this, we developed <i>tipFormer</i> (pollutan<i>t</i>-protein <i>i</i>nteraction <i>p</i>rediction based on trans<i>former</i>), a novel deep learning approach for predicting interactions between airborne organic pollutants and human proteins. The model incorporates dual pretrained language models to encode proteins and organic pollutants, coupled with cross-attention mechanisms to learn intricate interaction patterns underlying pollutant-protein binding. Rigorous validation demonstrated that tipFormer achieves state-of-the-art performance, with an AUC of 0.9787 on a test set. Furthermore, genome-wide transcriptomic analysis using human bronchial epithelial cells exposed to three representative airborne pollutants revealed significant concordance between tipFormer's predicted targets and the experimentally responsive genes, thereby supporting the model's biological relevance. By bridging large-scale computational predictions with transcriptomic validation, this study provides deeper mechanistic insight into the molecular basis of air pollution-related adverse outcomes.