Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.

ABSTRACT: A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentratio

SUBMITTER: Zelezniak A 

PROVIDER: S-EPMC6167078 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature