mRNASyner: An Integrative Framework for Full-Length mRNA Sequence Optimization via Multimodule Synergistic Design

Messenger RNA (mRNA) technology holds great promise in biomedicine; however, its therapeutic efficacy relies heavily on translation efficiency and the in vivo stability of mRNA sequences, both of which are governed by dynamic interactions between the coding and noncoding regions. Existing optimization approaches typically focus on individual sequence components and lack the capacity for global, synergistic optimization. Moreover, their performance is often limited when processing long sequences or supporting iterative sequence refinement. To overcome these challenges, we propose a unified and interpretable in silico framework for full-length mRNA sequence design, integrating CDS optimization, UTR generation, and degradation modeling. In silico case studies based on respiratory syncytial virus (RSV) vaccine design demonstrated that mRNASyner achieved a favorable balance between translational accessibility and structural stability. mRNASyner thus enables the design of full-length mRNA sequences, supports long-sequence optimization, and offers a novel solution for the development of personalized mRNA therapeutics.

Authors

Institutions

Publication Details

Journal
Molecular Pharmaceutics
Published
2026-04-16
DOI
https://doi.org/10.1021/acs.molpharmaceut.5c01705
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

mRNASyner: An Integrative Framework for Full-Length mRNA Sequence Optimization via Multimodule Synergistic Design

Changzhi Sun, Gang-Ao Wang, Kai Zhang, Shiliang Li et al.
Molecular Pharmaceutics
RNA and protein synthesis mechanisms
article

mRNASyner: An Integrative Framework for Full-Length mRNA Sequence Optimization via Multimodule Synergistic Design

Changzhi Sun, Gang-Ao Wang, Kai Zhang, Shiliang Li, Ziyan Feng, Zijie Gu
article en

Abstract

Messenger RNA (mRNA) technology holds great promise in biomedicine; however, its therapeutic efficacy relies heavily on translation efficiency and the in vivo stability of mRNA sequences, both of which are governed by dynamic interactions between the coding and noncoding regions. Existing optimization approaches typically focus on individual sequence components and lack the capacity for global, synergistic optimization. Moreover, their performance is often limited when processing long sequences or supporting iterative sequence refinement. To overcome these challenges, we propose a unified and interpretable in silico framework for full-length mRNA sequence design, integrating CDS optimization, UTR generation, and degradation modeling. In silico case studies based on respiratory syncytial virus (RSV) vaccine design demonstrated that mRNASyner achieved a favorable balance between translational accessibility and structural stability. mRNASyner thus enables the design of full-length mRNA sequences, supports long-sequence optimization, and offers a novel solution for the development of personalized mRNA therapeutics.

Molecular PharmaceuticsVol. 23(5)
East China University of Science and Technology (CN), Beijing Academy of Artificial Intelligence (CN), Drug Discovery Laboratory (Norway) (NO), Artificial Intelligence in Medicine (Canada) (CA), PRG S&Tech (South Korea), East China Normal University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 23%
RNA and protein synthesis mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.