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

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Item Type:Article
Title:A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
Creators Name:Piazza, I. and Beaton, N. and Bruderer, R. and Knobloch, T. and Barbisan, C. and Chandat, L. and Sudau, A. and Siepe, I. and Rinner, O. and de Souza, N. and Picotti, P. and Reiter, L.
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.
Keywords:Binding Sites, Botrytis, Cell Survival, Computational Biology, Drug Delivery Systems, Drug Discovery, HeLa Cells, Ligands, Machine Learning, Mass Spectrometry, Phosphotransferases, Protein Binding, Proteolysis, Proteome, Proteomics, Saccharomyces cerevisiae
Source:Nature Communications
Publisher:Nature Publishing Group
Page Range:4200
Date:21 August 2020
Official Publication:https://doi.org/10.1038/s41467-020-18071-x
PubMed:View item in PubMed

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