A combined strategy for high-efficiency expression of alkaline protease PrtA in Komagataella phaffii (syn. Pichia pastoris)

BACKGROUND: Microbial serine proteases are valuable for industrial applications due to broad substrate specificity and stability. However, heterologous overexpression in microbial hosts is often limited by cytotoxicity and poor secretion. This study developed an integrated strategy combining protein engineering and signal peptide optimization to enhance extracellular production of PrtA—a key acid-stable alkaline serine protease—in Komagataella phaffii. RESULTS: Directed evolution generated the Q245K variant, showing 1.35-fold higher extracellular expression than wild-type PrtA. A machine learning model, MPEPE (Mutation Predictor for Enhanced Protein Expression), was used to identify critical residues involved in protein secretion; saturation mutagenesis at the top-predicted site generated the I342D mutant with 1.48-fold improved productivity. The double mutant PrtA-Q245K/I342D achieved synergistic enhancement (1.84-fold higher secretion) without altering enzymatic properties. Evaluation of nine signal peptides revealed that serum albumin, α-factor (without pro-region), and PrtA’s native signal peptides each doubled the combinatorial mutant’s secretion, yielding 4.98-fold higher expression than the wild-type. In contrast, α-factor pro-region inclusion drastically reduced yields. In a 15-L fed-batch bioreactor, the optimized strain produced PrtA-Q245K/I342D at 4807.5 U/mL, equivalent to 1.5 g/L protein. CONCLUSIONS: The combined approach of directed evolution, machine learning-guided mutagenesis, and signal peptide engineering significantly boosted PrtA secretion while maintaining functional integrity. This strategy demonstrates strong potential for scalable industrial production of challenging heterologous proteases.