期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-18071-x
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资金
- Personalized Health and Related Technologies (PHRT) [PHRT-506]
- European Research Council Consolidator (ERC-CoG) [ERC-CoG 866004]
- Sinergia grant from the Swiss National Science Foundation (SNSF grant) [CRSII5_177195]
- European Union's Horizon 2020 research and innovation programme (ERC) [866004, 823839]
- Personalized Health and Related Technologies (PHRT) grant [PHRT-506]
- Sinergia grant from the Swiss National Science Foundation [CRSII5_177195]
- Swiss National Science Foundation (SNF) [CRSII5_177195] Funding Source: Swiss National Science Foundation (SNF)
- European Research Council (ERC) [866004] Funding Source: European Research Council (ERC)
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound. Proteomics is often used to map protein-drug interactions but identifying a drug's protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.
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