Efficient metabolic engineering and the development of mitochondrial therapeutics often rely upon the specific and strong import of foreign proteins into mitochondria. Fusing a protein to a mitochondria-bound signal peptide is a common method to localize proteins to mitochondria, but this strategy is not universally effective, with particular proteins empirically failing to localize. To help overcome this barrier, this work develops a generalizable and open-source framework to design proteins for mitochondrial import and quantify their specific localization. This Python-based pipeline quantitatively assesses the colocalization of different proteins previously used for precise genome editing in a high-throughput manner to reveal signal peptide-protein combinations that localize well in mitochondria.
Keywords: Python; digital image analysis; high-throughput imaging; mitochondria; protein engineering; subcellular localization.