Applied protein optimisation

Natural and laboratory selection can evolve proteins to exhibit highly optimised 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. Thus, lab evolution experiments may take years of tedious trial-and-error. We developed a suite of computational design algorithms to address the major problems of protein optimisation, including improving stability and protein expressibility, binding affinity and catalytic efficiency, and specificity. Our methods have had a central role in 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