Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok körútja 2, H-1117 Budapest, Hungary.
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany.
Nucleic Acids Res. 2023 Jan 6;51(D1):D517-D522. doi: 10.1093/nar/gkac928.
AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3D predictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/.
AI 驱动的蛋白质结构预测，尤其是 AlphaFold2（AF2），为结构生物学的几乎所有领域开辟了新的前沿。由于传统的跨膜蛋白结构预测方法既复杂又容易出错，因此 AF2 对该领域的研究人员帮助很大。AF2 为数千种新的α-螺旋膜蛋白提供了 3D 预测，弥补了相对较少的实验结构数量。然而，由于缺乏可靠的结构模板，以及 AF2 没有经过处理相界面的训练，因此需要对结构的正确性进行细致的评估。在我们的新数据库 Transmembrane AlphaFold database（TmAlphaFold database）中，我们应用了 TMDET，这是一种基于简单几何的方法，可以直观地显示膜平面最可能的位置。此外，我们还计算了几个参数来评估蛋白质在膜中的位置。这也使得 TmAlphaFold database 能够显示预测的 3D 结构是否真实。TmAlphaFold database 可在 https://tmalphafold.ttk.hu/ 上获取。
