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

Abstract

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 < 0.0001). Structural validation via redox-sensitive PAGE and nano-LC-MS/MS confirmed native disulfide pairing. The trained machine learning model demonstrated high predictive accuracy (R2 = 0.92, RMSE = 0.24 mg/L) with consistent performance across cross-validation folds (average R2 = 0.91 ± 0.02), and identified cytoplasmic redox potential and translational rate as the primary determinants of soluble expression. Conclusions We present an integrated platform that synergizes experimental high-throughput screening with predictive machine learning to overcome the challenge of ETA production. While validation on additional protein targets is needed to fully establish generalizability, this work establishes an optimized, scalable protocol for therapeutic-grade ETA and provides a transferable computational framework for the rational optimization of other complex, disulfide-rich proteins.