Evidence map›Paper›PMID 39608721›Full record

ReviewThe Journal of biological chemistry2025

mRNA vaccine sequence and structure design and optimization: Advances and challenges.

Lei Jin, Yuanzhe Zhou, Sicheng Zhang, Shi-Jie Chen

Abstract readReview
In one paragraph

Review in The Journal of biological chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026
    Review
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  4. 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
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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

4 authors.

Lei JinDepartment of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA.
Yuanzhe ZhouDepartment of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA.
Sicheng ZhangDepartment of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA.
Shi-Jie ChenDepartment of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA; Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA. Electronic address: chenshi@missouri.edu.

Funding

Center for Structural Biology of HIV RNAU54AI170660 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALICE TELESNITSKY · 2022 to 2026
$32.1M
New methods for computational modeling of RNA structuresR35GM134919 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI SHI-JIE CHEN · 2020 to 2026
$3.3M
NIAID NIH HHS U54 AI170660NIGMS NIH HHS R35 GM134919
6 · The paper itself

Abstract

Messenger RNA (mRNA) vaccines have emerged as a powerful tool against communicable diseases and cancers, as demonstrated by their huge success during the coronavirus disease 2019 (COVID-19) pandemic. Despite the outstanding achievements, mRNA vaccines still face challenges such as stringent storage requirements, insufficient antigen expression, and unexpected immune responses. Since the intrinsic properties of mRNA molecules significantly impact vaccine performance, optimizing mRNA design is crucial in preclinical development. In this review, we outline four key principles for optimal mRNA sequence design: enhancing ribosome loading and translation efficiency through untranslated region (UTR) optimization, improving translation efficiency via codon optimization, increasing structural stability by refining global RNA sequence and extending in-cell lifetime and expression fidelity by adjusting local RNA structures. We also explore recent advancements in computational models for designing and optimizing mRNA vaccine sequences following these principles. By integrating current mRNA knowledge, addressing challenges, and examining advanced computational methods, this review aims to promote the application of computational approaches in mRNA vaccine development and inspire novel solutions to existing obstacles.

Indexed as

COVID-19COVID-19 VaccinesmRNA VaccinesRNA, MessengerSARS-CoV-2Vaccines, SyntheticHumansCOVID-19 VaccinesmRNA VaccinesRNA, MessengerVaccines, Syntheticmachine-learningmRNA sequence designmRNA vaccineRNA structurevaccine design

Identifiers

PMID39608721
PMCPMC11728972

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.