These researchers engineer high affinity binding proteins using laboratory directed evolution. Hundreds of millions of protein mutants are produced and tested for functionality (stability and affinity to various targets of interest) on a weekly basis. Yet this seemingly high throughput does not approach the 1034 possible mutants. For this reason, the group is using computational estimation of protein stability and protein-protein affinity to guide their selection of which mutants to test in the laboratory.
This project uses several software packages, including Rosetta, FoldX, PyMol, and custom codes in Python and Linux. Multiple experimental questions will be asked; for example, how effectively do Rosetta and FoldX predict stabilization resulting from amino acid mutation? Stabilizing mutations will be predicted, and the proteins will be produced and tested for stability in the laboratory. Increased stability has many practical advantages including robustness to industrial use and reduced immunogenicity for clinical use. Moreover, mutants of different stability enable the researchers to assess whether parental protein stability aids evolvability – i.e., does a more stable protein improve the identification of novel functional mutants via directed evolution? Secondly, the researchers use structural and stability analysis to predict which protein topologies will be evolutionarily optimal for the generation of novel binding function.
Return to this PI's main page.