Protein-Protein Interactions and Protein Aggregation: From Scattering Experiments to Coarse-Grained Molecular Models

Marco Blanco (University of Delaware)

Colloidal protein-protein interactions (PPI) in solution have a dramatic effect on protein stability and protein self-assembly. Knowledge of PPI is important as they provide means to predict and control the behavior of proteins in applications such as protein phase behavior and aggregation of biopharmaceuticals. However, the nature of these interactions is poorly understood since it is affected by several factors such as solution conditions and protein sequence. Typically, PPI are characterized via the second osmotic viral coefficient (B22), as determined from Rayleigh light scattering (LS). Nevertheless, discrepancies and confusion have arisen regarding recent interpretations of B22 from classical treatment of LS data, as well as questions about refinement of coarse-grained (CG) models of PPI against experimental results such as B22. Here, an alternative treatment of Rayleigh scattering in multi-component systems is provided based on Kirkwood-Buff solution theory. The model is compared against experimental scattering data for acidic solutions of a-chymotrypsinogen from low to high protein concentrations. The results illustrating this approach quantitatively provide accurate values for protein interactions vs. protein concentration, from low to high concentrations, and without assumptions on the nature of interactions and/or the thermodynamics of the system.

Additionally, a simple, but realistic implicit-solvent CG model is designed to study the role of PPI on the stability and aggregation propensity of proteins. By explicitly considering the peptide backbone, as well as the most relevant physical features involved in the conformational stability of proteins (i.e., steric, hydrophobic, hydrogen-bond, and electrostatic interactions), this CG model allows one to fully capture structural and thermodynamic changes on proteins. The results from this CG model are extended to develop a new and reliable algorithm that screens proteins and rank them for their aggregation propensity. Aggregation-prone regions are identified by considering the individual contribution of each amino acid to the free-energy of self-association. The algorithm also provides "rules" in designing/selecting proteins for controlling aggregation, which allows gaining insight into the effects of specific interactions on aggregation propensity.

Back to Seminar Home Page

Last modified 05-May-2014 by website owner: NCNR (attn: )