Nature communicationsNov 12, 2025

Using deep learning to improve mRNA code for better protein production and treatment effectiveness

mRNA Technology Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Jun 27, 2026

Abstract

Essence

RiboDecode suggests data-driven codon design can improve mRNA translation and dose efficiency.

Evidence

This platform experiment combined in silico testing, in vitro expression assays, and mouse studies where optimized influenza hemagglutinin mRNA induced ten times stronger neutralizing antibody responses and optimized nerve growth factor mRNA gave equivalent neuroprotection at one-fifth dose.

Caveat

The therapeutic efficacy evidence comes from selected assays and mouse models rather than human clinical outcomes.

Simplified

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 mΨ-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. 1

Key numbers

6 to 16×

Increase in Protein Expression

Measured in vitro for -optimized sequences compared to unoptimized controls.

10×

Neutralizing Antibody Response

Compared to unoptimized influenza hemagglutinin mRNAs in vivo.

1/5

Dose Reduction for Neuroprotection

Achieved with optimized nerve growth factor mRNAs in an optic nerve crush model.

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Using deep learning to improve mRNA code for better protein production and treatment effectiveness

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