Electrostatically mediated protein-protein interactions (PPI) can influence key product properties such as solubility, solution viscosity, and aggregation rates. Predictive models would allow for candidates/formulations to be screened with little or no protein material. Three monoclonal antibodies that display qualitatively different experimental PPI were evaluated at a range of pH and ionic strength conditions that are typical of product formulations. PPI parameters (kD, B22, and G22) were obtained from static and dynamic light scattering measurements and spanned from strongly repulsive to strongly attractive net interactions. Coarse-grained (CG) molecular simulations of PPI (specifically, B22) were compared against experimental PPI parameters across multiple pH and salt conditions, using a CG model that treats each amino acid explicitly. Predicted B22 values with default model parameters matched experimental B22 values semiquantitatively for some cases; others required parameter tuning to account for effects such as ion binding. Experimental PPI values were also analyzed for each monoclonal antibody within the context of single-protein properties such as net charge, and domain-based and global dipole moments. The results show that PPI predicted qualitatively and semiquantitatively by CG molecular modeling of B22 can be an effective computational tool for molecule and formulation assessment.
Keywords: biophysical model(s); light scattering (dynamic); light scattering (static); monte carlo simulation(s); protein formulation(s).
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