The Role of Amino Acid Sequence in the Self-Association of Therapeutic Monoclonal Antibodies: Insights from Coarse-Grained Modeling
A Chaudhri and IE Zarraga and S Yadav and TW Patapoff and SJ Shire and GA Voth, JOURNAL OF PHYSICAL CHEMISTRY B, 117, 1269-1279 (2013).
Coarse-grained computational models of therapeutic monoclonal antibodies and their mutants can be used to understand the effect of domain-level charge-charge electrostatics on the self-association phenomena at high protein concentrations. The coarse-grained models are constructed for two antibodies at different coarse-grained resolutions by using six different concentrations. It is observed that a particular monoclonal antibody (hereafter referred to as MAb1) forms three-dimensional heterogeneous structures with dense regions or clusters compared to a different monoclonal antibody (hereafter referred to as MAb2) that forms homogeneous structures without clusters. The potential of mean force (PMF) and radial distribution functions (RDF) plots for the mutants (hereafter referred to as M1, M5, M7, and M10) show trends consistent with previously reported experimental observation of viscosities. The mutant referred to as M6 shows strongly attractive interactions that are consistent with previously reported negative second virial coefficients (B-22) obtained from light-scattering experiments (Yadav et al. Pharm. Res. 2011, 28, 1750-1764; Yadav et al. Mol. Pharmaceutics. 2012, 9, 791-802). Clustering data on MAb1 reveal a small number of large clusters that are hypothesized to be the reason for the high experimental viscosity. This is in contrast with M6 (that differs from MAb1 in only a few amino acids), where cluster analysis reveals the formation of a large number of smaller clusters that is hypothesized to be the reason for the observed lower viscosity. The coarse-grained representations are effective in picking up differences based on local charge distributions of domains to make predictions on the self- association characteristics of these protein solutions.
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