(308c) Design and Control of Protein-Protein Interactions to Enhance Stability and Solubility

Biopharmaceuticals are arguably the fastest growing segment of the innovator pharmaceutical pipeline, with a market value in 2010 of $140 billion for just the biotechnology sector alone, and represent a large and growing investment in pharmaceutical research and development.  Nonnative protein aggregation is a ubiquitous concern during biopharmaceutical product formulation and process development. The presence of even relatively small quantities of soluble or insoluble non-native aggregates may significantly increase product development time and expenses, as well as cause safety issues in the clinic. There is evidence that the size, structure, and/or morphology of aggregates are important factors in the biological response(s) to aggregates in vivo. Aggregation and phase separation of native or folded proteins is also a long-standing research area for pharmaceutical proteins, with great impact on both bioseparations and drug delivery. This presentation focuses on combined experimental, modeling, and protein engineering approaches to control or predict the effects of mutations and solvent conditions on aggregation and self-assembly of therapeutic and model proteins, as well as new approaches to predict high-concentration behavior from low-concentration light scattering experiments. Our complementary experimental and modeling approaches offer a unique perspective on these problems, and the results highlight an important interplay between thermodynamics and kinetics of folding, self-assembly, and amyloid formation. In addition, they underscore potential roles for coarse-grained molecular modeling to shed insight on engineering design strategies for multi-domain proteins, as well as predictive approaches for high-concentration protein systems of interest for delivery and manufacture of therapeutic proteins.  In particular, we find that seemingly minor changes in surface charge (via mutation or solvent conditions) can have dramatic affects on processes such as native dimerization, phase transitions, and the growth processes and phase separation for non-native aggregates.  Overall, the results illustrate opportunities to gain better control over native and non-native protein self-assembly via predictive engineering approaches.