Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy

Yupeng Li, Fan Wang, Jiaqi Yang, Zirong Han, Linfeng Chen, Wenbing Jiang, Hao Zhou, Tong Li, Zehua Tang, Jianxiang Deng, Xin He, Gaofeng Zha, Zhaoyu Hu, Yong Hu, Linping Wu, Changyou Zhan, Caijun Sun, Yao He () and Zhi Xie ()
Additional contact information
Yupeng Li: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Fan Wang: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Jiaqi Yang: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Zirong Han: Sun Yat-Sen University
Linfeng Chen: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Wenbing Jiang: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Hao Zhou: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Tong Li: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Zehua Tang: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Jianxiang Deng: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Xin He: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Gaofeng Zha: The Seventh Affiliated Hospital. Sun Yat-Sen University
Zhaoyu Hu: Rhegen Biotechnology Co., Ltd
Yong Hu: Rhegen Biotechnology Co., Ltd
Linping Wu: Chinese Academy of Sciences
Changyou Zhan: Fudan University
Caijun Sun: Sun Yat-Sen University
Yao He: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science
Zhi Xie: Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a deep learning framework that generates mRNA codon sequences for enhanced mRNA translation. RiboDecode introduces several advances, including direct learning from large-scale ribosome profiling data and generative exploration of a large sequence space. In silico analysis demonstrates RiboDecode’s robust predictive accuracy for unseen genes and cellular environments. In vitro experiments showed substantial improvements in protein expression, significantly outperforming past methods. In addition, RiboDecode enables mRNA design with consideration of cellular context and demonstrates robust performance across different mRNA formats, including m1Ψ-modified and circular mRNAs, an important feature for mRNA therapeutics. In vivo mouse studies showed that optimized influenza hemagglutinin mRNAs induce ten times stronger neutralizing antibody responses against influenza virus compared to the unoptimized sequence. In an optic nerve crush model, optimized nerve growth factor mRNAs achieve equivalent neuroprotection of retinal ganglion cells at one-fifth the dose of the unoptimized sequence. Collectively, RiboDecode represents a paradigm shift from rule-based to a data-driven, context-aware approach for mRNA therapeutic applications, enabling the development of more potent and dose-efficient treatments.

Date: 2025
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DOI: 10.1038/s41467-025-64894-x

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