Boob Aashutosh Girish, Tan Shih-I, Zaidi Airah, Singh Nilmani, Xue Xueyi, Zhou Shuaizhen, Martin Teresa A, Chen Li-Qing, Zhao Huimin
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Nat Commun. 2025 May 4;16(1):4151. doi: 10.1038/s41467-025-59499-3.
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.
线粒体在能量产生和新陈代谢中起着关键作用，使其成为代谢工程和疾病治疗的一个有前景的靶点。然而，尽管已知过客蛋白对定位效率有影响，但只有少数蛋白质定位标签被鉴定用于线粒体靶向。为了解决这一限制，我们利用变分自编码器设计新型线粒体靶向序列。计算机模拟分析表明，所生成的肽中有很大一部分（90.14%）具有功能，并拥有对线粒体靶向重要的特征。我们在四种真核生物中对人工肽进行了表征，并作为概念验证，证明了它们通过途径区室化提高3-羟基丙酸滴度以及分别将5-氨基酮戊酸合酶的递送提高1.62倍和4.76倍的效用。此外，我们利用潜在空间插值来揭示双靶向序列的进化起源。总体而言，我们的工作证明了生成式人工智能在线粒体生物学基础研究和实际应用中的潜力。
