An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway.

While natural terpenoid cyclases generate complex terpenoid structures via cationic mechanisms, alternative radical cyclization pathways are underexplored. The metal-catalysed H-atom transfer reaction (M-HAT) offers an attractive means for hydrofunctionalizing olefins, providing access to terpenoid-like structures. Artificial metalloenzymes offer a promising strategy for introducing M-HAT reactivity into a protein scaffold. Here we report our efforts towards engineering an artificial radical cyclase (ARCase), resulting from anchoring a biotinylated [Co(Schiff-base)] cofactor within an engineered chimeric streptavidin. After two rounds of directed evolution, a double mutant catalyses a radical cyclization to afford bicyclic products with a cis-5-6-fused ring structure and up to 97% enantiomeric excess. The involvement of a histidine ligation to the Co cofactor is confirmed by crystallography. A time course experiment reveals a cascade reaction catalysed by the ARCase, combining a radical cyclization with a conjugate reduction. The ARCase exhibits tolerance towards variations in the dienone substrate, highlighting its potential to access terpenoid scaffolds.

Principles and Methods in Computational Membrane Protein Design.

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.