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<p>Protein-ligand interactions are crucial for understanding various biological processes and drug discovery and design. However, experimental methods are costly; single-ligand-oriented methods are tailored to specific ligands; multi-ligand-oriented methods are constrained by the lack of ligand encoding. In this study, we propose a structure-based method called LABind, designed to predict binding sites for small molecules and ions in a ligand-aware manner. LABind utilizes a graph transformer to capture binding patterns within the local spatial context of proteins, and incorporates a cross-attention mechanism to learn the distinct binding characteristics between proteins and ligands. Experimental results on three benchmark datasets demonstrate both the effectiveness of LABind and its ability to generalize to unseen ligands. Further analysis validates that LABind can effectively integrate ligand information to predict binding sites. Additionally, the application of LABind is extended to binding site center localization, sequence-based methods, and molecular docking tasks.</p>

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LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Zhijun Zhang,
  • Lijun Quan,
  • Junkai Wang,
  • Liangchen Peng,
  • Qiufeng Chen,
  • Bei Zhang,
  • Lexin Cao,
  • Yelu Jiang,
  • Geng Li,
  • Liangpeng Nie,
  • Tingfang Wu,
  • Qiang Lyu

摘要

Protein-ligand interactions are crucial for understanding various biological processes and drug discovery and design. However, experimental methods are costly; single-ligand-oriented methods are tailored to specific ligands; multi-ligand-oriented methods are constrained by the lack of ligand encoding. In this study, we propose a structure-based method called LABind, designed to predict binding sites for small molecules and ions in a ligand-aware manner. LABind utilizes a graph transformer to capture binding patterns within the local spatial context of proteins, and incorporates a cross-attention mechanism to learn the distinct binding characteristics between proteins and ligands. Experimental results on three benchmark datasets demonstrate both the effectiveness of LABind and its ability to generalize to unseen ligands. Further analysis validates that LABind can effectively integrate ligand information to predict binding sites. Additionally, the application of LABind is extended to binding site center localization, sequence-based methods, and molecular docking tasks.