Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Nat Commun. 2021 Nov 11;12(1):6497. doi: 10.1038/s41467-021-26850-3.


Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antifungal Agents / pharmacology
  • Candida albicans / drug effects
  • Genome-Wide Association Study
  • Kinetochores / metabolism
  • Machine Learning*
  • Systems Biology / methods


  • Antifungal Agents