Evidence map›Paper›PMID 41224770›Full record

ArticleNature communications2025

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 and 9 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026
    Review
  2. Circular Photocaged mRNA for Light-induced Late-stage Activation of Translation.Angewandte Chemie (International ed. in English) · 2026
    Article
  3. Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.Molecular therapy : the journal of the American Society of Gene Therapy · 2026
    Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. ADAR-GPT: A continually fine-tuned language model for predicting A-to-I RNA editing sites.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Yupeng Li *State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Fan Wang *State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Jiaqi YangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Zirong HanSchool of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China.
Linfeng ChenState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Wenbing JiangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.ORCID http://orcid.org/0009-0008-1951-1763
Hao ZhouState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Tong LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Zehua TangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Jianxiang DengState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.ORCID http://orcid.org/0000-0002-7363-2274
Xin HeState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Gaofeng ZhaScientific Research Center, The Seventh Affiliated Hospital. Sun Yat-Sen University, Shenzhen, China.
Zhaoyu HuENO Bio mRNA Innovation Institute, Rhegen Biotechnology Co., Ltd, Shenzhen, China.
Yong HuENO Bio mRNA Innovation Institute, Rhegen Biotechnology Co., Ltd, Shenzhen, China.
Linping WuCenter for Chemical Biology and Drug Discovery, China-New Zealand Joint Laboratory of Biomedicine and Health, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.ORCID http://orcid.org/0000-0001-7579-2987
Changyou ZhanDepartment of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0002-5215-2829
Caijun SunSchool of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China.ORCID http://orcid.org/0000-0002-2000-7053
Yao HeState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China. scheyao@hotmail.com.ORCID http://orcid.org/0000-0002-0324-0267
Zhi XieState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China. xiezhi@gmail.com.ORCID http://orcid.org/0000-0002-5589-4836

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 m

Indexed as

CodonProtein BiosynthesisRNA, MessengerAnimalsAntibodies, NeutralizingHumansMiceMice, Inbred C57BLOrthomyxoviridae InfectionsRibosomesAntibodies, NeutralizingCodonRNA, Messenger

Identifiers

PMID41224770
PMCPMC12612108

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.