Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, Budapest, 1117, Hungary.
Department of Bioinformatics, Semmelweis University, Tűzoltó u. 7, Budapest, 1094, Hungary.
Sci Rep. 2023 Nov 20;13(1):20283. doi: 10.1038/s41598-023-47204-7.
AlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.
AlphaFold2 (AF2) 为每个已知或预测的蛋白质提供了一个 3D 结构，为结构生物学的几乎每个领域都开辟了新的前景。然而，与跨膜蛋白分子合作对科学家来说是一个臭名昭著的挑战，导致实验确定的结构数量有限。因此，在这个有限的训练集上训练的算法也面临困难。为了解决这个问题，我们最近推出了 TmAlphaFold 数据库，其中预测的 AlphaFold2 结构被嵌入到膜平面中，并使用几何评估为每个预测提供质量评估（嵌入结构的合理性）。在本文中，我们分析了与仅可获得实验结构时相比，AF2 如何提高了膜蛋白的结构覆盖率，当时高通量结构预测受到极大限制。我们还评估了 AF2 如何用于在高度多样化的蛋白质家族中搜索（遥远的）同源物。通过结合质量评估和同源搜索，我们可以确定 AF2 准确性仍然有限的蛋白质家族，需要进行实验结构测定。
