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Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins

View ORCID ProfileDorothee Childs, Karsten Bach, Holger Franken, View ORCID ProfileSimon Anders, Nils Kurzawa, Marcus Bantscheff, Mikhail Savitski, Wolfgang Huber
doi: https://doi.org/10.1101/373845
Dorothee Childs
1European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
4Cellzome GmbH, GlaxoSmithKline, Meyerhofstraße 1, 69117 Heidelberg, Germany
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  • ORCID record for Dorothee Childs
Karsten Bach
2Department of Pharmacology, University of Cambridge, CB2 1PD, Cambridge, UK
3Cancer Research UK Cambridge Cancer Centre, CB2 0RE, Cambridge, UK
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Holger Franken
4Cellzome GmbH, GlaxoSmithKline, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Simon Anders
5Center for Molecular Biology of Heidelberg University (ZMBH), Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
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Nils Kurzawa
1European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Marcus Bantscheff
4Cellzome GmbH, GlaxoSmithKline, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Mikhail Savitski
1European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Wolfgang Huber
1European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
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  • For correspondence: wolfgang.huber@embl.de
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Abstract

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.

Footnotes

  • ↵† Equal contributor

  • Refined descriptions of the results. Revised Figures S6 and S7.

  • Abbreviations
    CETSA
    Cellular thermal shift assay
    FDR
    False discovery rate
    H0
    Null hypothesis
    H1
    Alternative hypothesis
    NPARC
    Non-parametric analysis of response curves
    ROC
    Receiver operating characteristic
    RSS
    Residual sum of squares
    Tm
    Melting point
    TPP
    Thermal proteome profiling
  • Copyright 
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    Posted September 12, 2019.
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    Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins
    Dorothee Childs, Karsten Bach, Holger Franken, Simon Anders, Nils Kurzawa, Marcus Bantscheff, Mikhail Savitski, Wolfgang Huber
    bioRxiv 373845; doi: https://doi.org/10.1101/373845
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    Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins
    Dorothee Childs, Karsten Bach, Holger Franken, Simon Anders, Nils Kurzawa, Marcus Bantscheff, Mikhail Savitski, Wolfgang Huber
    bioRxiv 373845; doi: https://doi.org/10.1101/373845

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