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Virtual screening for small-molecule pathway regulators by image-profile matching.

Mohammad H Rohban1, Ashley M Fuller2, Ceryl Tan3

  • 1Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Cell Systems
|September 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel virtual screening method using cell imaging data to identify chemical regulators of biological pathways. This approach accelerates drug discovery by replacing costly, customized screens with an efficient, profile-based computational strategy.

Keywords:
Hippo/Yap1 pathwaycomputational drug discoverydrug screeningimage-based profilingvirtual screening

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Area of Science:

  • Computational Biology
  • Drug Discovery
  • Cellular Imaging

Background:

  • Identifying chemical regulators of biological pathways is crucial but faces bottlenecks in traditional screening methods.
  • Current methods like biochemical and cell-based screens are expensive, time-consuming, and disease-specific.

Purpose of the Study:

  • To develop and validate an uncustomized, virtual, profile-based screening approach for identifying chemical regulators.
  • To leverage public cell image data from the Cell Painting assay for pathway matching.

Main Methods:

  • Utilized a correlation-based computational strategy on Cell Painting assay image data.
  • Performed retrospective analysis to identify known small-molecule regulators.
  • Conducted prospective screening to discover novel compounds for specific genes.

Main Results:

  • Successfully identified known small-molecule regulators for 32% of positive-control gene queries retrospectively.
  • Discovered and validated new compounds for three query genes in prospective screening.
  • Identified compounds that modulate YAP1, including those that kill Yap1-dependent sarcoma cells.

Conclusions:

  • The image-profile-based screening approach offers a more efficient alternative to traditional, customized screens.
  • This method accelerates the discovery of biologically and therapeutically relevant compounds.
  • The approach has the potential to significantly reduce labor and resource intensity in drug discovery.