TY - JOUR
T1 - Recent Progress in Sequence Optimization of mRNA Vaccine
T2 - Biological Mechanism, Quantitative Metrics, and Computational Model
AU - Wang, Yunwei
AU - Cai, Yuheng
AU - Wu, Zhixing
AU - Zhang, Jingming
AU - Turtle, Lance
AU - Meng, Jia
N1 - Publisher Copyright:
© The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
PY - 2026/1/1
Y1 - 2026/1/1
N2 - As a groundbreaking advancement in vaccinology, messenger RNA (mRNA) vaccines have transformed the field by offering rapid, flexible, and scalable solutions for combating infectious diseases. However, the efficacy, stability, and immunogenicity of mRNA vaccines are highly dependent on the optimization of their sequences. Recent progress in synthetic biology and computational methods has enabled the optimization of mRNA sequences to enhance their properties, holding the promise to provide deeper insights into the design principles of effective mRNA vaccines. However, it remains a major challenge to determine how to best optimize mRNA sequences for diverse biological contexts and therapeutic applications. In this review, we provide an in-depth analysis of the current advancements in optimizing mRNA vaccine sequences, put forward a comprehensive overview of the latest computational and biological approaches in this field, with a particular focus on the biological mechanisms underlying mRNA translation efficiency and stability, highlighting several quantitative indicators that may affect vaccines’ performance, and summarize some methods to optimize mRNA vaccine by algorithms. We also propose the limitations of current models and the need for further research to address the complexity of biological systems.
AB - As a groundbreaking advancement in vaccinology, messenger RNA (mRNA) vaccines have transformed the field by offering rapid, flexible, and scalable solutions for combating infectious diseases. However, the efficacy, stability, and immunogenicity of mRNA vaccines are highly dependent on the optimization of their sequences. Recent progress in synthetic biology and computational methods has enabled the optimization of mRNA sequences to enhance their properties, holding the promise to provide deeper insights into the design principles of effective mRNA vaccines. However, it remains a major challenge to determine how to best optimize mRNA sequences for diverse biological contexts and therapeutic applications. In this review, we provide an in-depth analysis of the current advancements in optimizing mRNA vaccine sequences, put forward a comprehensive overview of the latest computational and biological approaches in this field, with a particular focus on the biological mechanisms underlying mRNA translation efficiency and stability, highlighting several quantitative indicators that may affect vaccines’ performance, and summarize some methods to optimize mRNA vaccine by algorithms. We also propose the limitations of current models and the need for further research to address the complexity of biological systems.
KW - 5′ UTR
KW - deep learning
KW - mean ribosome load
KW - mRNA vaccine
KW - sequence optimization
UR - https://www.scopus.com/pages/publications/105032755603
U2 - 10.1177/11779322261431255
DO - 10.1177/11779322261431255
M3 - Review article
AN - SCOPUS:105032755603
SN - 1177-9322
VL - 20
JO - Bioinformatics and Biology Insights
JF - Bioinformatics and Biology Insights
ER -
SDG 3 Good Health and Well-being