期刊
CELL SYSTEMS
卷 1, 期 6, 页码 383-395出版社
CELL PRESS
DOI: 10.1016/j.cels.2015.12.003
关键词
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资金
- Canada Research Chair in Molecular Studies of Antibiotics
- Howard Hughes Medical Institute International Research Scholar Award
- Royal Society Wolfson Research Merit Award
- Scottish Universities Life Sciences Alliance Research Chair
- Canadian Institutes for Health Research [MOP 119572]
- European Research Council [SCG-233457]
- Wellcome Trust [085178/Z/08/Z]
- National Institutes of Health [R01OD010929]
- Ministere de l'enseignement superieur, de la recherche, de la science et de la technologie du Quebec through Genome Quebec
- U.S. Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program [DAMD17-03-1-0471]
- Canada Research Chair in Systems and Synthetic Biology
- Wellcome Trust [085178/Z/08/Z] Funding Source: Wellcome Trust
The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
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