In this work, we present a computationally efficient method for predicting the binding affinity different orientations of a protein on a chromatographic surface. This method is powerful in that it identifies which regions on the protein surface play key roles in binding. Additionally, this method predicts the overall binding affinity of a given protein to a chromatographic surface and can thus be used to predict elution order and elution salt concentration. We first use short, all-atoms MD simulations to obtain the equilibrated bound structure of the protein of interest at 100 different orientations. We then use coarse-grained calculations to obtain the free energy of binding of each orientation. In this work, we demonstrate the ability of this method to predict key binding faces and elution order of a set of small, globular proteins. We validate our results by comparing the observed key binding faces to NMR experiments and elution order to chromatographic experiments. This method is then extended to a set of industrially relevant Fab and Fc regions. In order to investigate this method’s ability to discriminate between product related variants, we simulate a series of homologous Fab variants. In order to extend this method to mAbs, we develop a method for combining the free energies of binding of Fab and Fc regions. This method is then validated through comparison with chromatography experiments. The results of this analysis can be employed to predict selectivity trends as well as the potential of various MM resins for separating different product related mAb variants with important implications for the design of mAb domains for improved biomanufacturability.

Developing computational tools for understanding and predicting protein retention behavior in multimodal chromatographic systems

253rd National Meeting (2017)

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