Preventing protein aggregation and denaturation is an important challenge in the development of protein-based therapeutics as biological drugs become increasingly popular. Non-native aggregation causes degradation and shortens the shelf life of drugs while denaturation leads to a loss of biological activity. Excipients are often added to improve the stability of proteins and prevent aggregation. In this study, we illustrate a computational approach to characterize protein-excipient interactions using the Site Identification by Ligand Competitive Saturation (SILCS) technology. In SILCS, the functional group binding affinity pattern of a macromolecule, such as an antibody, is calculated through hybrid Grand Canonical Monte Carlo and molecular dynamics (GCMC/MD) based simulations that include small diverse chemical probes in explicit solvent,  The GCMC/MD method enhances the sampling of probe distribution around the protein surface and accelerates convergence of the simulation. 3D maps of the affinity of functional groups on the entire protein surface called FragMaps are obtained from the simulations. In the present study, pre-computed FragMaps were used to scan a large library of excipient molecules to identify the probability of the excipients interacting with the entire protein surface. In addition, protein surface patches that could be involved in protein-protein interaction were also evaluated allowing identification of excipient with a high probability of inhibiting protein aggregation. We present a case-study of how such results can be combined to interpret experimental protein stability data on small globular proteins (lysozyme and myoglobin) and the NIST standard Mab.

Excipient-protein interactions for enhancing the stability of protein-based therapeutics using the site identification by ligand competitive saturation technology

256th National Meeting (2018)

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