Comparative analysis of SUMO and his-tag fusion for soluble expression of the SARS-CoV-2 omicron RBD in E. coli.

The soluble expression of the SARS-CoV-2 Receptor-Binding Domain (RBD) is fundamental for manufacturing protein-based countermeasures. Although E. coli is a favored host for rapid production, its utility is often constrained by the formation of insoluble aggregates, thereby limiting the supply for vaccine and diagnostic development. Given the persistent threat of emerging SARS-CoV-2 variants, the need for reliable, high-yield expression platforms to overcome this solubility challenge remains critical. This study aimed to achieve soluble expression of the Omicron RBD variant in E. coli using a computationally guided strategy. To support solubility-enhancing tag selection, a machine learning-based tool (TagSolver), trained on the UESolDS dataset, predicted a 61.98 % solubility probability for the SUMO-RBD. Two constructs were designed: one containing a SUMO tag and a His-tagged RBD as a control. Both were expressed in E. coli SHuffle® T7 cells, and solubility was assessed by SDS-PAGE after 8 h of induction at 22 °C. The SUMO-RBD was expressed in a soluble form, whereas the His-tagged RBD was insoluble. Following purification and SUMO protease cleavage, FTIR analysis indicated a native-like secondary structure enriched in β-sheets. These findings demonstrate, for the first time, the soluble expression of the SARS-CoV-2 Omicron RBD in E. coli and establish a refolding-free strategy for producing this antigen. Furthermore, the TagSolver predictor was introduced, which may accelerate the selection of solubility-enhancing tags.