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

TL;DR

CrossBind is a novel multi-modal method that combines protein geometric structure and sequence knowledge to improve the accuracy of identifying nucleic-acid-binding residues, outperforming existing methods.

Contribution

The paper introduces CrossBind, a collaborative cross-modal approach utilizing contrastive learning and attention mechanisms to enhance binding residue prediction accuracy.

Findings

Outperforms state-of-the-art methods GraphSite and GraphBind

Achieves 10.8-17.3% higher F1-Score on DNA and RNA datasets

Improves Matthews correlation coefficient by up to 24.8%

Abstract

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.