ProSiteHunter: A unified framework for sequence-based prediction of protein-nucleic acid and protein-protein binding sites

Abstract

Accurate prediction of protein binding sites is essential for elucidating protein function, understanding molecular interaction mechanisms, and facilitating drug design. However, existing sequence-based approaches are often designed for specific binding-site types and therefore lack generality, whereas structure-based methods typically rely on high-quality structural models, limiting their applicability. Here, we introduce ProSiteHunter, a unified sequence-based framework for protein binding-site prediction, which integrates a fine-tuned protein language model (SiteT5) with a multi-source feature-fusion network that incorporates evolutionary, geometric, and statistical features, while employing bidirectional semantics, local associations, and global dependencies for comprehensive binding-site characterization. The method was systematically evaluated on diverse binding sites prediction tasks, where ProSiteHunter achieved a 39.1% average improvement in PRAUC for protein-DNA/RNA/protein tasks and a 7.4% PRAUC enhancement on the particularly challenging antibody-antigen task over state-of-the-art methods. Moreover, ProSiteHunter is capable of identifying local flexible sites that complement AlphaFold3 predictions and improving the accuracy of antibody-antigen interaction prediction. These results highlight ProSiteHunter as an efficient and unified approach for accurate and robust prediction of diverse protein binding sites.