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

Abstract

Despite the success of messenger RNA (mRNA) COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that uses transformer architectures tailored for mRNA coding sequences (CDSs) and untranslated regions (UTRs) to design mRNAs with enhanced expression and stability. GEMORNA-designed full-length mRNAs exhibited up to a 41-fold increase in firefly luciferase expression compared with 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 antitumor cytotoxicity in chimeric antigen receptor T cells. These advancements highlight the vast potential of deep generative artificial intelligence for mRNA therapeutics.