A novel deep generative model for mRNA vaccine development: Designing 5′ UTRs with N1-methyl-pseudouridine modification

Abstract

Efficient translation mediated by the 5′ untranslated region (5′ UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5′ UTR. We discovered that the optimal 5′ UTR for m1Ψ-modified mRNA (m1Ψ–5′ UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ–5′ UTRs rather than directly utilizing high-expression endogenous gene 5′ UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ–5′ UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5′ UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5′ UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5′ UTRs.

Keywords

• mRNA vaccine
• Machine learning
• 5′ UTR
• mRNA design
• Sequence design
• N1-Methyl-pseudouridine