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A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

Author

Listed:
  • Nils Kurzawa

    (Genome Biology Unit
    Heidelberg University)

  • Isabelle Becher

    (Genome Biology Unit)

  • Sindhuja Sridharan

    (Genome Biology Unit
    GlaxoSmithKline)

  • Holger Franken

    (GlaxoSmithKline)

  • André Mateus

    (Genome Biology Unit)

  • Simon Anders

    (Center for Molecular Biology of Heidelberg University (ZMBH))

  • Marcus Bantscheff

    (GlaxoSmithKline)

  • Wolfgang Huber

    (Genome Biology Unit)

  • Mikhail M. Savitski

    (Genome Biology Unit)

Abstract

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. However, current data analysis strategies use preset thresholds that can lead to suboptimal sensitivity/specificity tradeoffs and limited comparability across datasets. Here, we present a method based on statistical hypothesis testing on curves, which provides control of the false discovery rate. We apply it to several datasets probing epigenetic drugs and a metabolite. This leads us to detect off-target drug engagement, including the finding that the HDAC8 inhibitor PCI-34051 and its analog BRD-3811 bind to and inhibit leucine aminopeptidase 3. An implementation is available as an R package from Bioconductor ( https://bioconductor.org/packages/TPP2D ). We hope that our method will facilitate prioritizing targets from thermal profiling experiments.

Suggested Citation

  • Nils Kurzawa & Isabelle Becher & Sindhuja Sridharan & Holger Franken & André Mateus & Simon Anders & Marcus Bantscheff & Wolfgang Huber & Mikhail M. Savitski, 2020. "A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19529-8
    DOI: 10.1038/s41467-020-19529-8
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    Cited by:

    1. Dezerae Cox & Ching-Seng Ang & Nadinath B. Nillegoda & Gavin E. Reid & Danny M. Hatters, 2022. "Hidden information on protein function in censuses of proteome foldedness," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Florian P. Bayer & Manuel Gander & Bernhard Kuster & Matthew The, 2023. "CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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