Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder
Aashutosh Girish Boob, Shih‐I Tan, A. A. Zaidi, Nilmani Singh, Xueyi Xue, Shuaizhen Zhou, Teresa A. Martin, Li‐Qing Chen, Huimin Zhao
Abstract
Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology. Mitochondria play a key role in cellular metabolism. Here, authors develop a Variational Autoencoder to design novel mitochondrial targeting sequences, validating them across several eukaryotic organisms and demonstrating their utility in metabolic engineering and protein delivery.