Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Department of Structural Biology, University at Buffalo, Buffalo, NY, USA.
Nature. 2024 Jul;631(8020):449-458. doi: 10.1038/s41586-024-07601-y. Epub 2024 Jun 19.
De novo design of complex protein folds using solely computational means remains a substantial challenge. 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 receptors, 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.
使用纯粹的计算手段来重新设计复杂的蛋白质折叠仍然是一个巨大的挑战。在这里，我们使用一个强大的深度学习管道来设计复杂的折叠和可溶性整膜蛋白的类似物。独特的膜拓扑结构，如 G 蛋白偶联受体的拓扑结构，在可溶性蛋白质组中是找不到的，我们证明它们的结构特征可以在溶液中再现。生物物理分析表明设计的高热稳定性，实验结构显示出显著的设计精度。可溶性类似物被具有天然结构基序的功能化，作为将膜蛋白功能引入可溶性蛋白质组的概念验证，有可能为药物发现开辟新的途径。总之，我们已经设计了复杂的蛋白质拓扑结构，并在其中加入了来自膜蛋白的功能，具有很高的实验成功率，从而有效地扩展了功能性可溶性折叠空间。
