De novo design of complex protein folds using solely computational means remains a substantial challenge¹. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors², are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

仅使用计算手段从头设计复杂蛋白质折叠仍然是一个巨大的挑战 ¹ 。在这里，我们使用强大的深度学习流程来设计复杂的折叠和完整膜蛋白的可溶类似物。在可溶性蛋白质组中没有发现独特的膜拓扑结构，例如来自 G 蛋白偶联受体 ² 的膜拓扑结构，我们证明它们的结构特征可以在溶液中重现。生物物理分析证明了设计的高热稳定性，实验结构显示出卓越的设计准确性。可溶性类似物用天然结构基序进行功能化，作为将膜蛋白功能引入可溶性蛋白质组的概念证明，有可能实现药物发现的新方法。总之，我们设计了复杂的蛋白质拓扑结构，并用膜蛋白的功能丰富了它们，实验成功率很高，导致功能性可溶性折叠空间事实上的扩展。