  Accurate identification of protein nucleic-acid-binding residues poses a
significant challenge with important implications for various biological
processes and drug design. Many typical computational methods for protein
analysis rely on a single model that could ignore either the semantic context
of the protein or the global 3D geometric information. Consequently, these
approaches may result in incomplete or inaccurate protein analysis. To address
the above issue, in this paper, we present CrossBind, a novel collaborative
cross-modal approach for identifying binding residues by exploiting both
protein geometric structure and its sequence prior knowledge extracted from a
large-scale protein language model. Specifically, our multi-modal approach
leverages a contrastive learning technique and atom-wise attention to capture
the positional relationships between atoms and residues, thereby incorporating
fine-grained local geometric knowledge, for better binding residue prediction.
Extensive experimental results demonstrate that our approach outperforms the
next best state-of-the-art methods, GraphSite and GraphBind, on DNA and RNA
datasets 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.
