Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.
Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.
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DOI：
10.1016/j.jmb.2021.167377
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发表时间：
2022-01-30
影响因子：
5.6
通讯作者：
Elcock AH
中科院分区：
文献类型：
--
作者：
Wehrspan ZJ;McDonnell RT;Elcock AH
DeepMind的AlphaFold 2软件在高质量的3D蛋白质结构预测方面迎来了一场革命。在DeepMind团队最近的工作中，已经对21种生物的整个蛋白质组进行了结构预测，其中有超过360，000种结构可供下载。在这里，我们表明，成千上万的新的铁硫（Fe-S）簇和锌（Zn）离子的结合位点，可以确定在这些预测的结构，通过穷举所有潜在的配体结合方向。我们证明，AlphaFold 2通常使配体结合位点的高度特异性的预测：例如，仅由四个半胱氨酸侧链组成的结合位点分为三个簇，代表4Fe-4S簇，2Fe-2S簇或单个Zn离子的结合位点。我们进一步显示：（a）在UniProt中记录的大多数已知的Fe-S簇和Zn结合位点通过AlphaFold 2结构恢复，（B）AlphaFold 2和UniProt之间偶尔存在争议，AlphaFold 2预测高度可信的替代结合位点，（c）我们在E.大肠杆菌与先前的生物信息学预测一致，（d）此处预测为配体结合位点的一部分的半胱氨酸与通过化学蛋白质组学技术显示为高度反应性的那些半胱氨酸几乎没有重叠，以及（e）AlphaFold 2偶尔似乎在半胱氨酸之间建立错误的二硫键，而这些半胱氨酸应该与配体配位。这些结果表明，AlphaFold 2可能是蛋白质组功能注释的重要工具，这里提出的方法可能对预测其他配体结合位点有用。
DeepMind’s AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc (Zn) ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of ligand binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
3.4
作者：
Estellon, Johan;de Choudens, Sandrine Ollagnier;Vandenbrouck, Yves
通讯作者：
Vandenbrouck, Yves
影响因子：
2.9
作者：
Iwema, Thomas;Picciocchi, Antoine;Jacquamet, Lilian
通讯作者：
Jacquamet, Lilian
影响因子：
5.6
作者：
Elcock, AH
通讯作者：
Elcock, AH
DOI：
10.1093/database/baz006
发表时间：
2019-02-05
影响因子：
5.8
作者：
Ireland, Sam M.;Martin, Andrew C. R.
通讯作者：
Martin, Andrew C. R.