Advancing high-throughput antibody discovery and engineering
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The development of hybridoma technology nearly forty years ago set the foundation for the use of antibodies in the life sciences. Subsequent advances in recombinant DNA technology have allowed us to adapt antibody genes to various screening systems, greatly increasing the throughput and specialized applications for which these complex biomolecules can be adapted. While selection systems are a powerful tool for discovery and evolution, they can be slow and prone to unintended biases. We see computational approaches as an efficient process for rapid discovery and engineering of antibodies. This is particularly relevant for biodefense and emerging infectious disease applications, for which time is a valuable commodity. In the first chapter of this work, we examine computational protocols for ‘supercharging’ proteins. This process resurfaces the target protein, adding charged moieties to impart specialized functions such as thermoresistance and cell penetration. Current algorithms for resurfacing proteins are static, treating each mutation as an event within a vacuum. The net result is that while several variants can be created, each must be tested experimentally to ensure the resultant protein is functional. In many cases, the designed proteins were severely impaired or incapable of folding. We hypothesize that a more dynamic approach, keeping an eye on energetics and the consequences of mutations will yield a more efficient and robust method for supercharging, successfully adding charges to proteins while minimizing deleterious effects. We continue on this theme applying the successful algorithm to supercharging antibodies for increased function. Utilizing the MS2 model biosensor system, we rationally engineer charges onto the surface of an antibody fragment, increasing thermoresistance, minimizing destabilizing effects, and in some cases actually increasing affinity. Finally, we apply next-generation sequencing approaches to the rapid discovery of antibodies directed against the Zaire Ebolavirus species. We utilize a local immunization strategy to generate a polarized antibody repertoire that is then sequenced to provide a database of antigen-specific variants. This repertoire is probed in silico and individual antibodies selected for analysis, bypassing time- and resource-consuming selection experiments.