A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems

The a priori prediction of protein adsorption behavior has been a long-standing goal in several fields. In the present work, property-modeling techniques have been used for the prediction of protein adsorption thermodynamics in ion-exchange systems directly from crystal structure. Quantitative structure-property relationship models of protein isotherm parameters and Gibbs free energy changes in ion-exchange systems were generated by using a support vector machine regression technique. The predictive ability of the models was demonstrated for two test-set proteins not included in the model training set. Molecular descriptors selected during model generation were examined to gain insights into the important physicochemical factors influencing stoichiometry, equilibrium, steric effects, and binding affinity in protein ion-exchange systems. The a priori prediction of protein isotherm parameters can have direct implications for various ion-exchange processes. As proof of concept, a multiscale modeling approach was used for predicting the chromatographic separation of a test set of proteins using the isotherm parameters obtained from the quantitative structure-property relationship models. The simulated column separation showed good agreement with the experimental data. The ability to predict chromatographic behavior of proteins directly from their crystal structures may have significant implications for a range of biotechnology processes.