Nature Communications (Nov 2021)

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

  • Ci Fu,
  • Xiang Zhang,
  • Amanda O. Veri,
  • Kali R. Iyer,
  • Emma Lash,
  • Alice Xue,
  • Huijuan Yan,
  • Nicole M. Revie,
  • Cassandra Wong,
  • Zhen-Yuan Lin,
  • Elizabeth J. Polvi,
  • Sean D. Liston,
  • Benjamin VanderSluis,
  • Jing Hou,
  • Yoko Yashiroda,
  • Anne-Claude Gingras,
  • Charles Boone,
  • Teresa R. O’Meara,
  • Matthew J. O’Meara,
  • Suzanne Noble,
  • Nicole Robbins,
  • Chad L. Myers,
  • Leah E. Cowen

DOI
https://doi.org/10.1038/s41467-021-26850-3
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 18

Abstract

Read online

The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans, define the function of three uncharacterized essential genes, and identify the target of a new antifungal compound.