摘要：SummaryAcetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets inE. coli,S. cerevisiae,and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.Graphical abstractDisplay OmittedHighlights•CAROM predicts PTM targets in a condition based on enzyme & reaction properties•Growth-limiting enzymes are preferential targets of acetylation•Isozymes and futile-cycles are associated with phosphorylation•CAROM reveals a ‘division of labor’ and a unique regulatory role for each PTMMachine learning; Metabolic flux analysis; Metabolic regulation; Omics; Systems biology
