Proteomics has emerged as a crucial tool in plant metabolic engineering, providing comprehensive insights into the protein landscape of engineered plants . This highthroughput approach enables the identification and quantification of thousands of proteins simultaneously, offering a direct view of the functional state of cells . In metabolic engineering, proteomics can reveal unexpected changes in protein abundance or posttranslational modifications resulting from genetic manipulations, helping to explain phenotypic outcomes and identify potential bottlenecks in engineered pathways . For instance, Fan et al. (2016) used stable isotope labeling and high-resolution mass spectrometry to analyze protein turnover in Arabidopsis, revealing how protein dynamics change in response to environmental stresses . Such approaches can be invaluable in assessing the impact of metabolic engineering on the broader cellular context, guiding refinements in engineering strategies, and uncovering new targets for manipulation. Moreover, integrating proteomic data with other omics datasets can provide a systemslevel understanding of metabolic networks, essential for predicting and optimizing the outcomes of metabolic engineering efforts.