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<p>Understanding the mechanism of action (MoA) of bioactive compounds is a central challenge in drug discovery and chemical biology. We propose a strategy for integrating morphological data with proteomics to provide deeper insights into the MoA of compounds. We combine the rich phenotypic profiles of Cell Painting (CP) with the unbiased protein target detection of Thermal Proteome Profiling (TPP), and construct protein–protein interaction networks based on potential targets identified using both assays. We validated our method with public TPP datasets for the five compounds (+)-JQ1, I-BET151, Vemurafenib, Crizotinib and Panobinostat, and public Proteome Integral Solubility Alteration (PISA) data for 49 compounds, together with CP data for 5259 drugs on U2OS cells. We show that the combined approach could accurately identify known MoAs for four out of five validation compounds and known targets for 29 out of 49 compounds. Finally, we deployed our method to characterize sinomenine, a compound with elusive knowledge of MoA. Our findings revealed novel facets of sinomenine's biological activity and highlight the value of multimodal profiling for chemical biology.</p>

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Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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  • Camilla Johansson,
  • Martin Johansson,
  • Phanindra Babu Kasi,
  • Mårten Larsson,
  • Per-Johan Jakobsson,
  • Ulf Göransson,
  • Jordi Carreras Puigvert,
  • Ola Spjuth,
  • Erik T Jansson

摘要

Understanding the mechanism of action (MoA) of bioactive compounds is a central challenge in drug discovery and chemical biology. We propose a strategy for integrating morphological data with proteomics to provide deeper insights into the MoA of compounds. We combine the rich phenotypic profiles of Cell Painting (CP) with the unbiased protein target detection of Thermal Proteome Profiling (TPP), and construct protein–protein interaction networks based on potential targets identified using both assays. We validated our method with public TPP datasets for the five compounds (+)-JQ1, I-BET151, Vemurafenib, Crizotinib and Panobinostat, and public Proteome Integral Solubility Alteration (PISA) data for 49 compounds, together with CP data for 5259 drugs on U2OS cells. We show that the combined approach could accurately identify known MoAs for four out of five validation compounds and known targets for 29 out of 49 compounds. Finally, we deployed our method to characterize sinomenine, a compound with elusive knowledge of MoA. Our findings revealed novel facets of sinomenine's biological activity and highlight the value of multimodal profiling for chemical biology.