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Accurate identification of protein nucleic-acid-binding residues poses asignificant challenge with important implications for various biologicalprocesses and drug design. Many typical computational methods for proteinanalysis rely on a single model that could ignore either the semantic contextof the protein or the global 3D geometric information. Consequently, theseapproaches may result in incomplete or inaccurate protein analysis. To addressthe above issue, in this paper, we present CrossBind, a novel collaborativecross-modal approach for identifying binding residues by exploiting bothprotein geometric structure and its sequence prior knowledge extracted from alarge-scale protein language model. Specifically, our multi-modal approachleverages a contrastive learning technique and atom-wise attention to capturethe positional relationships between atoms and residues, thereby incorporatingfine-grained local geometric knowledge, for better binding residue prediction.Extensive experimental results demonstrate that our approach outperforms thenext best state-of-the-art methods, GraphSite and GraphBind, on DNA and RNAdatasets by 10.8/17.3% in terms of the harmonic mean of precision and recall(F1-Score) and 11.9/24.8% in Matthews correlation coefficient (MCC),respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues