Advanced machine learning-guided optimization platform for high-yield soluble expression of Pseudomonas aeruginosa exotoxin A in engineered Escherichia coli strains

Background: The recombinant production of Pseudomonas aeruginosa exotoxin A (ETA), a critical component for immunotoxin development, remains hindered by its complex disulfide bond architecture, cytotoxicity, and aggregation propensity. Despite recent advancements in strain engineering, a systematic, data-driven approach integrating high-throughput screening with machine learning for ETA optimization has remained largely unexplored. Methods: We implemented a combinatorial optimization platform, screening 12 engineered E. coli strains across a matrix of four induction temperatures, three chaperone systems, and four redox-modulating additives. A high-throughput fluorescence-based solubility reporter was developed for rapid screening of 576 unique conditions, followed by training of an XGBoost machine learning model to predict soluble yield. The model was validated using 5-fold cross-validation with hyperparameter optimization to mitigate overfitting. Statistical analyses included one-way ANOVA with Tukey post-hoc test, Pearson correlation, and multiple regression. Results: The disulfide-competent strain SHuffle T7, induced at 12°C with co-expression of the DnaKJE/GroEL chaperone system and supplementation with 2 mM oxidized glutathione, yielded 3.24 ± 0.4 mg/L of soluble, enzymatically active ETA. This represents a 15-fold improvement over conventional BL21(DE3) systems (F (11,24) = 45.32, p