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Empowering drug off-target discovery with metabolic and structural analysis

Author

Listed:
  • 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
    University of California, San Diego
    Technical University of Denmark)

  • Eugene I. Shakhnovich

    (Harvard University)

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.

Suggested Citation

  • Sourav Chowdhury & Daniel C. Zielinski & Christopher Dalldorf & Joao V. Rodrigues & Bernhard O. Palsson & Eugene I. Shakhnovich, 2023. "Empowering drug off-target discovery with metabolic and structural analysis," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38859-x
    DOI: 10.1038/s41467-023-38859-x
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    References listed on IDEAS

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    1. Ofer Fridman & Amir Goldberg & Irine Ronin & Noam Shoresh & Nathalie Q. Balaban, 2014. "Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations," Nature, Nature, vol. 513(7518), pages 418-421, September.
    2. Scott A Becker & Bernhard O Palsson, 2008. "Context-Specific Metabolic Networks Are Consistent with Experiments," PLOS Computational Biology, Public Library of Science, vol. 4(5), pages 1-10, May.
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