Natural selection can evolve proteins to exhibit highly optimised activities. In recent decades, scientists have emulated natural evolutionary processes in the lab, enabling the optimisation of proteins for human needs and generating efficient enzymes and binders for basic and applied research. 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. Thus, lab evolution experiments may take years of tedious trial-and-error.
We developed several methods that enable rapid, one-shot optimisation of protein activities, generating enzymes that degrade a broad spectrum of highly toxic nerve agents, antibodies with much-improved affinity and stability, and even a much cheaper and more stable variant of a protein that is the prime candidate to serve as a vaccine for malaria. We are also deeply interested in designing enzymes for sustainability and alternative energy research.
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.
- 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.