Computational design of mRNA vaccines

Yoo Ah Kim, Kambiz Mousavi, Amirali Yazdi, Magda Zwierzyna, Marco Cardinali, Dillion Fox, Thomas Peel, Jeff Coller, Kunal Aggarwal, Giulietta Maruggi

Research output: Contribution to journalArticlepeer-review

Abstract

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines’ target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

Original languageEnglish (US)
Pages (from-to)1831-1840
Number of pages10
JournalVaccine
Volume42
Issue number7
DOIs
StatePublished - Mar 7 2024

Keywords

  • Optimization
  • Vaccines
  • mRNA sequence
  • mRNA structure

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • General Immunology and Microbiology
  • Infectious Diseases
  • Molecular Medicine
  • General Veterinary

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