PPBind-3D/1D: Protein-Protein Binding Affinity Prediction for Structural and Sequence Data

Abstract Background One key step of protein drug development is the screening of protein-protein binding (PPB) affinity. Various deep learning methods have been proposed to predict PPB affinity, but they are often challenged by data leakage, data scarcity, data heterogeneity, and structure-sequence gap. Methods To address these challenges in PPB affinity prediction, we propose a comprehensive framework by developing prediction models, PPBind-3D and PPBind-1D, to predict PPB affinity. PPBind-3D leverages structural information near the protein-protein binding interface to make its predictions. Additionally, PPBind-1D was developed using sequence data to address the lack of structural data in practical applications. Results PPBind-3D, equipped with monotonic neural network-constrained multi-task learning and Self-and-Mutual Attention, demonstrated state-of-the-art performance in PPB prediction across diverse datasets under both strict and random data partition settings. PPBind-1D, trained by aligning to PPBind-3D, overcame structural data scarcity to deliver robust sequence-based predictions while maintaining high accuracy, enabling practical deployment where structural data is unavailable. To facilitate real-world application, PPBind-3D was integrated into an online tool, which is available at https://rddc.tsinghua-gd.org/tool/dg-prediction. Finally, we demonstrated three application cases of our AI models in the virtual screening of protein drugs, further confirming the framework's utility and scalability. Conclusions Comparative and ablation studies demonstrate that PPBind-3D surpasses conventional benchmark approaches. Utilizing solely sequence information, PPBind-1D closely approximates the performance of PPBind-3D, thereby providing a viable alternative when structural data are inaccessible. Validation across three virtual screening case studies confirms the efficacy of both models, illustrating that our models can significantly facilitate high-throughput screening.