Prediction and Understanding of Protein Affinity

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Cramer0418413It has been a long standing goal in the bioseparations field to connect molecular-level investigations of protein adsorption to the macroscopic/process scale. This project will focus on the development of novel methods to enable the a priori prediction of protein binding affinity to ion-exchange chromatographic systems under a variety of conditions. High throughput experimentation will be employed to accumulate a large amount of chromatographic data. A computationally efficient tool will be developed that incorporates fundamental water-mediated interactions between individual amino acids and peptides with critical surface sites such as charged ligands for the prediction of protein-surface interaction free energies. Novel molecular descriptors will be developed and employed in advanced machine learning methods to construct models of increasingly complex experimental systems. Initial experimental and theoretical work will focus on well-defined surfaces such as self assembled monolayers (SAMs) and polyelectrolyte multilayers. The techniques and insights developed with these model systems will then be applied to more complicated ion-exchange chromatographic systems. Experiments will be carried out to produce a large set of linear and non-linear chromatographic adsorption data that will form the basis for two kinds of complementary chromatographic modeling work. The synergy of these methods provides a unique opportunity to develop powerful predictive tools and methods of gaining significant insight into the fundamental physics of the protein ion-exchange process. This will have a major impact on the ability to design efficient ion-exchange chromatography (IEC) based protein separation processes and will have applicability beyond chromatography to several fields where protein adsorption plays an important role such as biomaterials and drug delivery.