Published in

Nature Research, Nature Communications, 1(12), 2021

DOI: 10.1038/s41467-021-26850-3

Links

Tools

Export citation

Search in Google Scholar

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

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Red circle
Postprint: archiving forbidden
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

AbstractFungal 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.