GPT-driven generation and biological activity evaluation of novel mRNA trinucleotide Cap1 analogs for mRNA vaccine or immunotherapy

Abstract

Analogs of the mRNA 5′-cap are indispensable for mRNA translation, stability, translation efficiency, and immunogenicity, with emerging potential applications in novel preventive and therapeutic interventions. Here, this study presents a novel approach for designing mRNA Cap1 analogs with optimized biological activity. We leveraged the power of generative pre-trained transformer (GPT) architecture to generate novel cap analog sequences. A discriminative model is then employed to select promising candidates based on their predicted expression levels. Our results demonstrate that the GPT-based generative model significantly outperforms a traditional recurrent neural network (RNN) in terms of perplexity, indicating its superior ability to generate diverse and accurate cap analog sequences. Furthermore, the expression screening model achieves high accuracy in identifying potential high-expression candidates. Then, we synthesized a set of designed novel trinucleotide mRNA Cap1 analogs with modified ribose and incorporated it into mRNA using T7 polymerase. A series of experiments revealed that mRNA capped with YK-CAP-01–06 analogs exhibited increased translation efficiency and decapping enzyme stability compared to the commercially available cap-analog-capped mRNA. Finally, the potential application value was explored by constructing OVA, RSV preF- and VZV gE-mRNA vaccines, which resulted in significant (vs. controls) inhibition of tumor growth and an increase in IgG antibody levels in mice.