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Peer-reviewedCC BYJournal of the Royal Society, Interface

A multi-objective sequence engineering framework for rational genetic vaccine design.

  • Je Hun Moon1
  • Seo-Young Park1
  • Kok Siong Ang2
  • Yoon Ki Kim3
  • Dong-Yup Lee1
  1. 1School of Chemical Engineering, Sungkyunkwan University , Suwon, Republic of Korea.
  2. 2Centre for Biomedical Data Science, Duke-NUS Medical School , Singapore, Singapore.
  3. 3Department of Biological Sciences, KAIST , Daejeon, Republic of Korea.
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Abstract

Genetic vaccine performance is determined not only by the delivery platforms and antigen choice but also by the coding sequence that influences antigen expression, innate immune sensing, transcript stability and sequence-associated risks. However, the design principles governing these sequence-level effects remain insufficiently defined. Thus, we consider vaccine coding-sequence design as a multi-objective engineering problem that requires simultaneous consideration of multiple, potentially competing biological properties. We present a multi-parameter computational framework for analysing SARS-CoV-2 vaccine coding sequences, including BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), AZD1222 (AstraZeneca) and several academic vaccine candidates. The framework evaluates codon usage, codon-pair context, CpG content, regulatory motifs, GC content and localized sequence-structural features within a unified quantitative design space. Our analysis shows that existing vaccines use diverse design strategies and that no single metric adequately captures their design complexity. By enabling systematic comparison across multiple parameters, the framework supports the rational generation and prioritization of candidate sequences with defined biological trade-offs. This work bridges computational sequence analysis, vaccine biology and engineering design, providing a foundation for context-aware optimization of genetic vaccine sequences across delivery platforms, target tissues and emerging pathogens.

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Cite

BibTeXRIS
BibTeX
@article{moon2026multiobjective,
  title = {{A multi-objective sequence engineering framework for rational genetic vaccine design.}},
  author = {Je Hun Moon and Seo-Young Park and Kok Siong Ang and Yoon Ki Kim and Dong-Yup Lee},
  journal = {Journal of the Royal Society, Interface},
  year = {2026},
  month = oct,
  doi = {10.1098/rsif.2026.0525},
  url = {https://europepmc.org/article/MED/42901509},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A multi-objective sequence engineering framework for rational genetic vaccine design.
AU  - Je Hun Moon
AU  - Seo-Young Park
AU  - Kok Siong Ang
AU  - Yoon Ki Kim
AU  - Dong-Yup Lee
PY  - 2026
DA  - 2026/10/01
JO  - Journal of the Royal Society, Interface
DO  - 10.1098/rsif.2026.0525
UR  - https://europepmc.org/article/MED/42901509
AB  - Genetic vaccine performance is determined not only by the delivery platforms and antigen choice but also by the coding sequence that influences antigen expression, innate immune sensing, transcript stability and sequence-associated risks. However, the design principles governing these sequence-level effects remain insufficiently defined. Thus, we consider vaccine coding-sequence design as a multi-objective engineering problem that requires simultaneous consideration of multiple, potentially competing biological properties. We present a multi-parameter computational framework for analysing SARS-CoV-2 vaccine coding sequences, including BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), AZD1222 (AstraZeneca) and several academic vaccine candidates. The framework evaluates codon usage, codon-pair context, CpG content, regulatory motifs, GC content and localized sequence-structural features within a unified quantitative design space. Our analysis shows that existing vaccines use diverse design strategies and that no single metric adequately captures their design complexity. By enabling systematic comparison across multiple parameters, the framework supports the rational generation and prioritization of candidate sequences with defined biological trade-offs. This work bridges computational sequence analysis, vaccine biology and engineering design, providing a foundation for context-aware optimization of genetic vaccine sequences across delivery platforms, target tissues and emerging pathogens.
SN  - 1742-5689
SN  - 1742-5662
ER  -