Deep generative models design mRNA sequences with enhanced translational capacity and stability

Abstract

Despite the success of mRNA COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that utilizes Transformer architectures tailored for mRNA coding sequences (CDSs) and untranslated regions (UTRs), to design novel mRNAs with enhanced expression and stability. GEMORNA-designed full-length mRNAs exhibited up to a 41-fold increase in firefly luciferase expression compared to an optimized benchmark in vitro. GEMORNA-generated therapeutic mRNAs achieved up to a 15-fold enhancement in human erythropoietin (EPO) expression and substantially elicited antibody titers of COVID vaccine in mice. Additionally, GEMORNA's versatility extends to circular RNA, substantially enhancing circular EPO expression and boosting anti-tumor cytotoxicity in CAR-T cells. These advancements highlight deep generative AI's vast potential for mRNA therapeutics.