In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that 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 Escherichia coli are consistent with the design models, and the three 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 and enzymes.

在假环蛋白中，例如 TIM 桶、β 桶和一些螺旋跨膜通道，单个亚基以循环模式重复，形成一个中央空腔，可作为配体结合或酶活性的口袋。受这些蛋白质的启发，我们设计了一种基于深度学习的方法，从重复次数和长度的规范开始广泛探索闭合重复蛋白质的空间。在大肠杆菌中产生的 38 种结构多样的假环设计的生物物理数据与设计模型一致，并且我们能够获得的三种晶体结构与设计结构非常接近。对接研究表明，折叠和中央口袋的多样性为设计小分子粘合剂和酶提供了有效的起点。