Targeted proteomics is a convenient method determining enzyme expression levels, but a quantitative analysis of these proteomic data has not been fully explored yet. Here, we present and demonstrate a computational tool (principal component analysis of proteomics, PCAP) that uses quantitative targeted proteomics data to guide metabolic engineering and achieve higher production of target molecules from heterologous pathways. The method is based on the application of principal component analysis to a collection of proteomics and target molecule production data to pinpoint specific enzymes that need to have their expression level adjusted to maximize production. We illustrated the method on the heterologous mevalonate pathway in Escherichia coli that produces a wide range of isoprenoids and requires balanced pathway gene expression for high yields and titers. PCAP-guided engineering resulted in over a 40% improvement in the production of two valuable terpenes. PCAP could potentially be productively applied to other heterologous pathways as well. Display omitted •We report quantitative analysis method for proteomics data using PCA.•Principal component analysis of proteomics (PCAP) can direct metabolic engineering.•PCAP provides information not apparent by the simple observation of proteomics data.•PCAP was successfully applied to improve the mevalonate pathway (MEV) in E. coli.•Limonene and bisabolene production increased about 40% using PCAP-based strategy.