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

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.

Authors

• Linglin Jing Shanghai Artifcial Intelligence Laboratory Department of Computer Science, Loughborough University
• Sheng Xu Beihang University
• Yifan Wang Tsinghua University, Tsinghua University
• Yuzhe Zhou SSE & FNII, The Chinese University of Hong Kong (Shenzhen)
• Tao Shen
• Zhigang Ji Shanghai Jiao Tong University
• Hui Fang Shanghai University of Finance and Economics
• Zhen Li Beijing Institute of Technology
• Siqi Sun Research Institute of Intelligent Complex Systems, Fudan University Shanghai Artificial Intelligence Laboratory

Keywords

• CV: Biometrics, Face, Gesture & Pose
• CV: Medical and Biological Imaging
• CV: Multi-modal Vision