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Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.
Fu, Ci; Zhang, Xiang; Veri, Amanda O; Iyer, Kali R; Lash, Emma; Xue, Alice; Yan, Huijuan; Revie, Nicole M; Wong, Cassandra; Lin, Zhen-Yuan; Polvi, Elizabeth J; Liston, Sean D; VanderSluis, Benjamin; Hou, Jing; Yashiroda, Yoko; Gingras, Anne-Claude; Boone, Charles; O'Meara, Teresa R; O'Meara, Matthew J; Noble, Suzanne; Robbins, Nicole; Myers, Chad L; Cowen, Leah E.
Afiliación
  • Fu C; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Zhang X; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Veri AO; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Iyer KR; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Lash E; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Xue A; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Yan H; Department of Microbiology and Immunology, UCSF School of Medicine, San Francisco, CA, 94143, USA.
  • Revie NM; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Wong C; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1X5, Canada.
  • Lin ZY; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1X5, Canada.
  • Polvi EJ; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Liston SD; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • VanderSluis B; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
  • Hou J; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Yashiroda Y; Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
  • Gingras AC; RIKEN Center for Sustainable Resource Science, Wako, Saitama, 351-0198, Japan.
  • Boone C; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • O'Meara TR; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1X5, Canada.
  • O'Meara MJ; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1M1, Canada.
  • Noble S; Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
  • Robbins N; RIKEN Center for Sustainable Resource Science, Wako, Saitama, 351-0198, Japan.
  • Myers CL; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
  • Cowen LE; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
Nat Commun ; 12(1): 6497, 2021 11 11.
Article en En | MEDLINE | ID: mdl-34764269
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-ß-thiophenylacrylamide (NP-BTA), an antifungal compound.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido