Abstract

In pseudocyclic proteins such as TIM barrels, {beta} barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity which can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in E. coli are consistent with the design models, and two crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small molecule binders or enzymes.