Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics
The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.
Authors
- Zunyun Fu
- Kun Wang (ORCID: https://orcid.org/0000-0001-8923-8557)
- Runze Zhang (ORCID: https://orcid.org/0000-0001-9698-0178)
- Xiangtai Kong (ORCID: https://orcid.org/0000-0002-6257-2560)
- Zimei Zhang (ORCID: https://orcid.org/0000-0003-3428-9482)
- Xiaochu Tong
- Xutong Li (ORCID: https://orcid.org/0000-0001-9547-0643)
- Ruifeng Huo
- Zhaokun Wang (ORCID: https://orcid.org/0000-0001-5895-3718)
- Mingyue Zheng (ORCID: https://orcid.org/0000-0002-3323-3092)
- Ning Qu (ORCID: https://orcid.org/0000-0001-5110-6719)
- Yingying Zhang (ORCID: https://orcid.org/0000-0002-4022-1145)
- Zhengyang Chen
- Yitian Wang (ORCID: https://orcid.org/0000-0003-3064-1020)
- Xiaolong Wu (ORCID: https://orcid.org/0009-0003-7150-808X)
- Sulin Zhang (ORCID: https://orcid.org/0000-0002-9167-4689)
- Shengkun Ni
- Ruirui Yang (ORCID: https://orcid.org/0000-0001-6510-4327)
- Wei Zhang (ORCID: https://orcid.org/0009-0004-7855-7949)
- Jiangshan Shi
Institutions
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Shanghai Institute of Materia Medica (CN)
Publication Details
- Journal
- Cell Genomics
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.xgen.2024.100655
- Citations
- 13
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Shanghai University of Traditional Chinese Medicine
- Shanghai City Youth Science and Technology Star Project
- National Key Research and Development Program of China
- Youth Innovation Promotion Association