Optimizing mRNA sequence design through computational models enhances translation efficiency and structural stability, addressing current challenges in mRNA vaccine development.
This review highlights computational approaches and key principles for optimizing mRNA vaccine sequence and structure to improve stability and translation efficiency.
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.
Jin et al. (Tue,) conducted a review in Communicable diseases and cancers. mRNA vaccine sequence and structure design was evaluated. Optimizing mRNA sequence design through computational models enhances translation efficiency and structural stability, addressing current challenges in mRNA vaccine development.