Applied protein optimisation
Natural and laboratory selection can optimise protein activities. But evolution is an iterative process in which every change in a protein (mutation) must result in a variant that is at least as functional as its predecessor or it would be purged by the powerful forces of selection, and lab evolution experiments may take years of tedious trial and error. We developed a suite of computational design algorithms to address major problems in protein optimisation, including improving stability and protein expressibility, antibody stability and humanness, binding affinity and catalytic efficiency, and specificity. Our methods have enabled optimising very challenging enzymes and binding proteins for therapeutics, vaccines, and green chemistry applications.
One of our most important goals is to enable broad use of our algorithms by biochemists and protein engineers. To that end, we develop web servers that allow researchers around the world to customise our design protocols for their particular needs. The web servers carry out the calculations on our lab's computer cluster and return models of improved binders and enzymes by email. Recently, we've shown that deep-learning-based structure prediction algorithms, such as AlphaFold, can be used as reliable starting points for our design algorithms (Barber-Zucker 2022). This means that, in principle, any of the 300 million protein sequences that are deposited in genomic databases can be subjected to one-shot protein optimisation, realising one of the most significant long-term goals of protein engineering and design.
You can find a list of papers and patents that use our algorithms here and a tutorial here. You're most welcome to try these web servers yourself!
Further reading
- Goldenzweig, A.; Goldsmith, M.; Hill, S. E.; Gertman, O.; Laurino, P.; Ashani, Y.; Dym, O.; Unger, T.; Albeck, S.; Prilusky, J.; Lieberman, R. L.; Aharoni, A.; Silman, I.; Sussman, J. L.; Tawfik, D. S.; Fleishman, S. J. Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability. Mol. Cell 2016, 63 (2), 337–346.
- Campeotto, I.; Goldenzweig, A.; Davey, J.; Barfod, L.; Marshall, J. M.; Silk, S. E.; Wright, K. E.; Draper, S. J.; Higgins, M. K.; Fleishman, S. J. One-Step Design of a Stable Variant of the Malaria Invasion Protein RH5 for Use as a Vaccine Immunogen. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (5), 998–1002.
- Khersonsky, O.; Lipsh, R.; Avizemer, Z.; Ashani, Y.; Goldsmith, M.; Leader, H.; Dym, O.; Rogotner, S.; Trudeau, D. L.; Prilusky, J.; Amengual-Rigo, P.; Guallar, V.; Tawfik, D. S.; Fleishman, S. J. Automated Design of Efficient and Functionally Diverse Enzyme Repertoires. Mol. Cell 2018, 72 (1), 178–186.e5.
- Goldenzweig, A.; Fleishman, S. J. Principles of Protein Stability and Their Application in Computational Design. Annu. Rev. Biochem. 2018, 87, 105–129.
- Weinstein, J.; Khersonsky, O.; Fleishman, S. J. Practically Useful Protein-Design Methods Combining Phylogenetic and Atomistic Calculations. Curr. Opin. Struct. Biol. 2020, 63, 58–64.
- Barber-Zucker, S.; Mindel, V.; Garcia-Ruiz, E.; Weinstein, J. J.; Alcalde, M.; Fleishman, S. J. Stable and Functionally Diverse Versatile Peroxidases Designed Directly from Sequences. J. Am. Chem. Soc. 2022, 144, 3564–3571.
- Tennenhouse, A.; Khmelnitsky, L.; Khalaila, R.; Yeshaya, N.; Noronha, A.; Lindzen, M.; Makowski, E. K.; Zaretsky, I.; Sirkis, Y. F.; Galon-Wolfenson, Y.; Tessier, P. M.; Abramson, J.; Yarden, Y.; Fass, D.; Fleishman, S. J. Computational Optimization of Antibody Humanness and Stability by Systematic Energy-Based Ranking. Nat Biomed Eng 2023.