Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Aashutosh Girish Boob, Shih-I Tan, Airah Zaidi, Nilmani Singh, Xueyi Xue, Shuaizhen Zhou, Teresa A. Martin, Li-Qing Chen and Huimin Zhao ()
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Aashutosh Girish Boob: University of Illinois at Urbana-Champaign
Shih-I Tan: University of Illinois at Urbana-Champaign
Airah Zaidi: University of Illinois at Urbana-Champaign
Nilmani Singh: University of Illinois at Urbana-Champaign
Xueyi Xue: University of Illinois at Urbana-Champaign
Shuaizhen Zhou: University of Illinois at Urbana-Champaign
Teresa A. Martin: University of Illinois at Urbana-Champaign
Li-Qing Chen: University of Illinois at Urbana-Champaign
Huimin Zhao: University of Illinois at Urbana-Champaign

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-59499-3

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