ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions

Yuxuan Wang, Ying Xia, Junchi Yan, Ye Yuan, Hong-Bin Shen and Xiaoyong Pan ()
Additional contact information
Yuxuan Wang: Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China
Ying Xia: Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China
Junchi Yan: Shanghai Jiao Tong University
Ye Yuan: Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China
Hong-Bin Shen: Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China
Xiaoyong Pan: Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-43597-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43597-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-43597-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().