Co-cultivation of yeast to enhance carboxypeptidase enzyme production: biocalorimetric, respirometric, and deep learning investigation

Abstract

Carboxypeptidase Y (CPDY) production by Kluyveromyces marxianus is typically limited by ethanol accumulation, which inhibits enzyme yield. In this study, this challenge was addressed by co-cultivating K. marxianus with Saccharomyces cerevisiae to scavenge ethanol and thus enhance CPDY production. CPDY is an industrially important exopeptidase used in food processing, pharmaceutical and biorefinery applications. Using Response Surface Methodology (RSM), key medium components (yeast nitrogen base, ammonium chloride, dextrose) in shake-flask fermentations and a 2 L biocalorimetric reactor were optimized. The co-culture showed a clear division of labor such that, K. marxianus produced CPDY and ethanol from dextrose, and S. cerevisiae rapidly consumed the ethanol, preventing accumulation. As a result, CPDY activity was sustained longer, achieving enzyme titers roughly double those of individual cultures. Maximum CPDY activities of 8 U/mL (shake flask) and 16 U/mL (biocalorimeter) were obtained, with corresponding biomass concentrations of 3 g/L and 4.5 g/L. Real-time metabolic heat flow (calorimetry) and off-gas analysis revealed distinct dextrose and ethanol utilization phases, confirming the co-culture’s synergistic metabolism. Additionally, a custom Long Short-Term Memory (LSTM) neural network (“CoYeast-LSTM”) was developed to model the bioprocess. The LSTM accurately predicted key metabolic transitions and CPDY yields with a mean squared error MSE of 0.0060 and mean absolute error MAE of 0.071. This integrated approach, combining co-culture fermentation with advanced monitoring yielded high CPDY activity and improved real-time process control. The findings illustrate a novel and sustainable strategy for enzyme production through microbial co-cultivation coupled with real-time bioprocess monitoring and predictive modelling.