High-throughput metabolomics predicts drug-target relationships for eukaryotic proteins

A recent "Molecular Systems Biology" paper by the Sauer group (IMSB) uses high-throughput metabolomics for the prediction of drug-target relationships for 86 targets in the eukaryote Saccharomyces cerevisiae, including a novel inhibition of GPR1 signaling by the drug ibuprofen.

by Dominic Dähler
Graphical abstract Sauer paper March 1 2022
The Sauer group uses high-throughput metabolomics to predict drug-target relationships in the yeast Saccharomyces cerevisiae. (From: https://t.co/SD6QcLlU7m)

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high-throughput metabolomics and overexpression to predict drug–target interactions.

The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high-throughput metabolomics.

Link to the paper in external pageMolecular Systems Biology

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