Empowering drug off-target discovery with metabolic and structural analysis

Sourav Chowdhury, Daniel C. Zielinski, Christopher Dalldorf, Joao V. Rodrigues, Bernhard O. Palsson and Eugene I. Shakhnovich ()
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Sourav Chowdhury: Harvard University
Daniel C. Zielinski: University of California, San Diego
Christopher Dalldorf: University of California, San Diego
Joao V. Rodrigues: Harvard University
Bernhard O. Palsson: University of California, San Diego
Eugene I. Shakhnovich: Harvard University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Date: 2023
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DOI: 10.1038/s41467-023-38859-x

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