A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Ilaria Piazza, Nigel Beaton, Roland Bruderer, Thomas Knobloch, Crystel Barbisan, Lucie Chandat, Alexander Sudau, Isabella Siepe, Oliver Rinner, Natalie de Souza, Paola Picotti () and Lukas Reiter ()
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Ilaria Piazza: ETH Zürich, Institute of Molecular Systems Biology, Department of Biology
Nigel Beaton: Biognosys AG
Roland Bruderer: Biognosys AG
Thomas Knobloch: Bayer SAS, Crop Science Division
Crystel Barbisan: Bayer SAS, Crop Science Division
Lucie Chandat: Bayer SAS, Crop Science Division
Alexander Sudau: Bayer SAS, Crop Science Division
Isabella Siepe: BASF SE
Oliver Rinner: Biognosys AG
Natalie de Souza: ETH Zürich, Institute of Molecular Systems Biology, Department of Biology
Paola Picotti: ETH Zürich, Institute of Molecular Systems Biology, Department of Biology
Lukas Reiter: Biognosys AG

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18071-x

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