Multimodal (MM) chromatography provides a powerful means to enhance the selectivity of protein separations by taking advantage of multiple weak interactions that include direct charge-charge interactions, hydrophobic assembly, interaction between dipoles, etc. In order to increase our understanding of such phenomena, the binding of proteins to model MM chromatographic surfaces was examined. The MM surface in our model was represented by a self assembled monolayer (SAM) presenting MM ligands with fixed surface density. In order to ascertain binding free energies, we followed two parallel approaches: (I) The first approach entailed all atoms molecular dynamics simulations along with umbrella sampling methods to ascertain the binding free energies of different faces of the protein. First, two faces of the protein ubiquitin were tested. The free energy profiles thus obtained were compared to single molecule force spectroscopy experiments of the same faces of the protein, which showed qualitative similarities. Following the same procedure to evaluate the binding of two faces of cytochrome-c to the MM SAM revealed mechanisms of binding that were very similar to the binding of chemically analogous faces of ubiquitin to the same SAM. (II) The second approach involved calculations of interaction energies that accounted for electrostatics, dispersion and hydrophobic interactions through the use of appropriate coarse grained force fields at various orientations of the protein with respect to the multimodal SAM surface. Using these theoretical interaction energies, binding maps were generated. Thermal averaging of these interaction energies yielded binding free energies at various salt concentrations which displayed semi-quantitative agreement with isocratic chromatographic retention data. These two methods provide an excellent combination of assessing the binding of proteins to chromatographic surfaces and are both predictive in nature, thus, providing efficient tools for bioprocess development.

Binding of proteins to multimodal surfaces: From fundamental molecular modeling to prediction of chromatographic retention

251st National Meeting (2016)

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