Paul Blessy, Weeratunga Saroja, Tillu Vikas A, Hariri Hanaa, Henne W Mike, Collins Brett M
Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD, Australia.
Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, United States.
Front Cell Dev Biol. 2022 Feb 3;10:826688. doi: 10.3389/fcell.2022.826688. eCollection 2022.
Recent advances in protein structure prediction using machine learning such as AlphaFold2 and RosettaFold presage a revolution in structural biology. Genome-wide predictions of protein structures are providing unprecedented insights into their architecture and intradomain interactions, and applications have already progressed towards assessing protein complex formation. Here we present detailed analyses of the sorting nexin proteins that contain regulator of G-protein signalling domains (SNX-RGS proteins), providing a key example of the ability of AlphaFold2 to reveal novel structures with previously unsuspected biological functions. These large proteins are conserved in most eukaryotes and are known to associate with lipid droplets (LDs) and sites of LD-membrane contacts, with key roles in regulating lipid metabolism. They possess five domains, including an N-terminal transmembrane domain that anchors them to the endoplasmic reticulum, an RGS domain, a lipid interacting phox homology (PX) domain and two additional domains named the PXA and PXC domains of unknown structure and function. Here we report the crystal structure of the RGS domain of sorting nexin 25 (SNX25) and show that the AlphaFold2 prediction closely matches the experimental structure. Analysing the full-length SNX-RGS proteins across multiple homologues and species we find that the distant PXA and PXC domains in fact fold into a single unique structure that notably features a large and conserved hydrophobic pocket. The nature of this pocket strongly suggests a role in lipid or fatty acid binding, and we propose that these molecules represent a new class of conserved lipid transfer proteins.
使用机器学习进行蛋白质结构预测方面的最新进展，如AlphaFold2和RosettaFold，预示着结构生物学的一场革命。全基因组范围内的蛋白质结构预测为其结构和结构域内相互作用提供了前所未有的见解，并且在评估蛋白质复合物形成方面的应用已经取得了进展。在此，我们对包含G蛋白信号调节结构域的分选连接蛋白（SNX-RGS蛋白）进行了详细分析，提供了一个关键例子，说明AlphaFold2能够揭示具有先前未被怀疑的生物学功能的新结构。这些大蛋白在大多数真核生物中保守，已知与脂滴（LDs）和LD-膜接触位点相关，在调节脂质代谢中起关键作用。它们具有五个结构域，包括一个将它们锚定在内质网上的N端跨膜结构域、一个RGS结构域、一个与脂质相互作用的PX（phox同源）结构域以及另外两个结构和功能未知的结构域，称为PXA和PXC结构域。在此我们报道了分选连接蛋白25（SNX25）的RGS结构域的晶体结构，并表明AlphaFold2的预测与实验结构紧密匹配。通过分析多个同源物和物种中的全长SNX-RGS蛋白，我们发现距离较远的PXA和PXC结构域实际上折叠成一个独特的单一结构，其显著特征是有一个大的保守疏水口袋。这个口袋的性质强烈表明其在脂质或脂肪酸结合中起作用，并且我们提出这些分子代表一类新的保守脂质转运蛋白。
