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LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks.
Xia, Ying; Pan, Xiaoyong; Shen, Hong-Bin.
  • Xia Y; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
  • Pan X; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. Electronic address: 2008xypan@sjtu.edu.cn.
  • Shen HB; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. Electronic address: hbshen@sjtu.edu.cn.
J Mol Biol ; 435(13): 168091, 2023 07 01.
Article in English | MEDLINE | ID: covidwho-2305888
ABSTRACT
Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http//www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https//github.com/YYingXia/LigBind/ for academic use.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Protein Binding Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: J Mol Biol Year: 2023 Document Type: Article Affiliation country: J.jmb.2023.168091

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Protein Binding Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: J Mol Biol Year: 2023 Document Type: Article Affiliation country: J.jmb.2023.168091