Conclusion
In conclusion, our data shows that computational modeling can be used to gain meaningful insight into structural properties of an antibody that is critical for process development. This high through-put structural information can be used as an additional parameter to select antibodies that will lead to successful development downstream. While the overall structural features are important, a better mechanistic understanding of protein-chromatographic resin is gained by coupling in silicodocking studies to molecular dynamics and experimental data. We demonstrated using this approach that different regions of an antibody contribute at different levels to the overall column retention and selectivity. This is because different high affinity binding sites were observed base on the ligand density of the agarose-ligand complex. Further, we addressed the impact of ligand density on overall protein-resin binding and how the attached ligands to the resin compensate to maximize its interaction with the protein. Higher overall binding affinities are achieved at higher ligand densities by a cumulative effect of lower individual binding affinities (avidity) (Figure 4). This computational approach to evaluating protein ligand interaction has broader implications for biologics development (from lead candidate selection through purification to formulation) and commercial chromatographic resin design and head groups selection and optimization.