SCIENCE@home

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

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

Published 2025 in Nature Communications

ABSTRACT

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. Here, authors present LABind, a ligand-aware deep learning framework for predicting binding sites of small molecules and ions, with the capacity to generalize to unseen ligands.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-57 of 57 references · Page 1 of 1

CITED BY

Showing 1-7 of 7 citing papers · Page 1 of 1