In Silico Structural Dynamics and Downstream Bioprocess of Recombinant Human SUMO-IL-33

Interleukin-33 (IL-33) is a therapeutic target in inflammatory diseases, yet inhibitor screening is hindered by high commercial protein costs. While expressing IL-33 as a SUMO fusion protein overcomes yield limitations in Escherichia coli, whether this intact chimera can directly serve as a screening surrogate remains unresolved. Here, we combined in silico simulations with empirical bioprocess optimization to establish a high-yield pipeline for native human IL-33 production. AI-based structure predictions (AlphaFold and RoseTTAFold) revealed distinct inter-domain dynamics mediated by a flexible linker. Molecular dynamics simulations of the compact RoseTTAFold model suggested potential steric occlusion near receptor-binding interfaces, whereas AlphaFold models exhibited extended conformations, highlighting model-dependent variations that warrant further structural verification. To restore accessibility, tag cleavage was optimized using 1.0% (mol/mol) SUMO protease at 4 °C for 1 h, achieving near-complete digestion. Sandwich ELISA demonstrated an 87.6% molar epitope recognition recovery for cleaved IL-33 versus only 12.0% for the intact fusion, providing empirical evidence of substantial functional epitope masking by the SUMO tag. Two-step IMAC purification yielded ~19% mass recovery (~32.6% molar recovery). These findings demonstrate that tag removal is operationally essential for IL-33 functional availability in screening applications.