In this study, the binding of multimodal chromatographic ligands to the IgG1 FC domain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated 15N-labeled FC domain indicated that while single mode ion exchange ligands interacted very weakly throughout the FC surface, multimodal ligands interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the Fc. The multimodal ligand binding sites on the FC were concentrated in the hinge region and near the interface of the CH2 and CH3 domains. Further, the multimodal binding sites were primarily composed of positively charged, polar and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on FC corroborated molecular level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand-FC binding in these preferred regions was shown to be electrostatic interactions and pi-pi stacking of surface exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular level understanding of multimodal ligand-FC interactions and sets the stage for future analyses of even more complex biotherapeutics.

The goal of this research is to leverage computational molecular biophysics to guide process development, reduce experimental burden and focus purification activities on feasible targets. Here, we distill a complex separation problem (e.g. chromatographic retention of monoclonal antibodies) into a tangible model (ligand/protein complex), which is computationally feasible while preserving enough detail (atomistic level for interaction site) to support industrially relevant separation challenges. Computational docking, coupled with molecular dynamics simulation, produces results that are directionally consistent with chromatography for proteins (mAb). This approach is generalizable and can be applied to a range of ligands (AEX, CEX, and Mixed Mode). A detailed model of the chromatography base matrix (agarose) was constructed to obtain a biophysical understanding of potential protein/base matrix interactions. The base matrix was then modified in silico with ligands over a range of ligand densities representative of commercial chromatography resins to generate an agarose/ligand complex. A generic approach was developed to model the impact of avidity and ligand density on mAb/ligand interaction. The results revealed that increasing ligand density mask contributions of base matrix binding. Increasing the number of ligands that can interact with mAb results in more favorable free energy of binding or ΔG (more negative) with a limited incremental increase in ΔG by increasing N (number of ligands per agarose cluster) above three. Additionally, for protein/ligand interactions at each binding site, not all ligands contribute equally to the binding affinities or interaction energies and a redistribution of binding interactions/energies occur as N increases. These observations yield insights into the impact of avidity on retention (macroscopic affinity measurement via k’). The generic approach described in this manuscript can be leveraged to inform resin selection and design as well as targeted ligand selection/purification development in a rational manner.