Abstract:
: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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Piazza I,Beaton N,Bruderer R,Knobloch T,Barbisan C,Chandat L,Sudau A,Siepe I,Rinner O,de Souza N,Picotti P,Reiter Ldoi
10.1038/s41467-020-18071-xsubject
Has Abstractpub_date
2020-08-21 00:00:00pages
4200issue
1issn
2041-1723pii
10.1038/s41467-020-18071-xjournal_volume
11pub_type
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