AI-aided engineering and production optimization of chimeric hetero-viral capping enzymes for mRNA vaccines

The 5’ cap structure exerts profound impacts on mRNA vaccine stability, translational efficiency, and evasion of innate immunity, encompassing key enzymatic components: triphosphatase (TPase), guanylyl transferase (GTase), and bifunctional methyltransfer-ases (N7G-MTase/2’-O-MTase). However, conventional multi-enzyme capping systems for mRNA vaccines are plagued by inherent inefficiencies. Herein, we report an AI-driven novel hetero-viral chimeric capping enzyme for mRNA vaccines, developed through bioinformatics mining of the DeepSeek and UniProt databases. This single-molecule biocatalyst integrates all capping functions by fusing a rabbit fibroma virus (RFV)-derived TPase/GTase domain with bifunctional methyltransferase domains from flavivirus (FV), respiratory syncytial virus (RSV), and rotavirus (RV). Following systematic tag-host optimization, a soluble and functional RFV-FV chimera was achieved via N-terminal maltose-binding protein (MBP) fusion in E. coli BL21(DE3) pLysS, whereas RFV-RSV and RFV-RV chimeras formed insoluble aggregates or were trapped in chaperone complexes. Complementary assays confirmed that the RFV-FV capping enzyme exhibited catalytic activities comparable to the commercial vaccinia virus-derived capping enzyme: TPase (96.76%±1.63%), GTase (100.69%±2.95%), and MTase (77.87%±3.85%). Our work presents a versatile cross-viral RFV-FV capping enzyme that enables large-scale prokaryotic production and ensures mRNA vaccine quality via its multifunctional catalytic activity, thereby streamlining mRNA vaccine manufacturing and establishing an AI-driven paradigm for designer enzyme engineering.