Prediction of equilibrium phase compositions and β-glucosidase partition coefficient in aqueous two-phase systems

Artificial neural networks (ANN) and Flory-Huggins (F-H)-type models were implemented to simulate the binodal curve of an aqueous two-phase, system (ATPS) composed of poly(ethylene glycol), potassium phosphate, and water. The ANN model outperformed the F-H model in predicting the equilibrium compositions of the PEG-rich phase (average percent deviation: 10.0 versus 56.6). However, the estimation of interaction parameters was feasible only in the thermodynamic framework. Beta-glucosidase was introduced into the system under various temperature (25°-50°C) and pH conditions (6.5-8.0). The β-glucosidase partition coefficient increased with the temperature and pH over a range of 0.11-1.18. The network was better suited to predict the partitioning behavior of the enzyme because of the increased number of interaction parameters. The artificial intelligence-guided approach for isolating the enzyme has the potential to reduce costs, improve performance, and identify the most favorable purification conditions.