Machine learning assisted QSAR analysis to predict protein adsorption capacities on mixed-mode resins

Abstract

Mixed-mode chromatography is promising for protein separation, but the structural diversities of proteins result in distinct adsorption behaviors. This study used machine learning methods to establish the quantitative structure–activity relationships (QSAR) between the protein adsorption capacity on mixed-mode resins and the molecule properties of target proteins and mixed-mode ligands. Four mixed-mode resins and twenty proteins were tested at different pHs and salt concentrations. Two machine learning models, random forest and gradient boosting, were developed successfully to predict protein adsorption capacities. The determination coefficients (R2) of the training dataset, validation dataset, and test dataset ranged around 0.94–0.97, 0.79–0.82, and 0.90–0.93, respectively. Several key descriptors that have significant impacts on adsorption capacities were identified by a two-step descriptor elimination method. Moreover, the SHapley Additive exPlanations (SHAP) method was used to reveal the mechanism of target protein adsorption on mixed-mode resins. The results provided a valuable guidance for the design and selection mixed-mode resins for the separation and purification of target proteins.