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

Nils Kurzawa, Isabelle Becher, Sindhuja Sridharan, Holger Franken, André Mateus, Simon Anders, Marcus Bantscheff (), Wolfgang Huber () and Mikhail M. Savitski ()
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Nils Kurzawa: Genome Biology Unit
Isabelle Becher: Genome Biology Unit
Sindhuja Sridharan: Genome Biology Unit
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

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19529-8

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