AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.
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DOI：
10.1038/s41467-022-29394-2
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发表时间：
2022-04-01
影响因子：
16.6
通讯作者：
Skolnick J
中科院分区：
文献类型：
--
作者：
Gao M;Nakajima An D;Parks JM;Skolnick J
准确描述蛋白质之间的相互作用对于理解生物系统是必不可少的。最近，AlphaFold2(AF2)计算出了单个蛋白质非常精确的原子结构。在这里，我们证明了来自AF2的针对单个蛋白质序列开发的相同的神经网络模型可以适用于预测多聚体蛋白质复合体的结构，而不需要重新训练。与常用的方法不同，我们的方法AF2Complex不需要配对的多个序列比对。它实现了比一些复杂的蛋白质-蛋白质对接策略更高的准确性，并提供了对AF-Mulmer的显著改进，AF-Mulmer是AlphaFold针对多聚蛋白质的发展。此外，我们引入了预测任意蛋白质对之间直接蛋白质-蛋白质相互作用的度量标准，并在一些具有挑战性的基准集和大肠杆菌蛋白质组上验证了AF2Complex。最后，以细胞色素c生物发生系统I为例，我们给出了由该系统的8个成员组成的三个受欢迎的组件的高置信度模型。准确描述蛋白质之间的相互作用对于理解生物系统是必不可少的。在这里，作者提出了AF2Complex，并展示了对E.Coli细胞色素生物发生系统I的应用，为三个受欢迎的组件产生了可信的计算模型。
Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system. Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Here the authors present AF2Complex and show that application to the E. coli cytochrome biogenesis system I yields confident computational models for three sought-after assemblies.
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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
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Hassabis D
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5.6
作者：
Gong, Weikang;Guerler, Aysam;Zhang, Yang
通讯作者：
Zhang, Yang
影响因子：
2.9
作者：
Chen, R;Li, L;Weng, ZP
通讯作者：
Weng, ZP
影响因子：
3.6
作者：
Feissner, Robert E.;Richard-Fogal, Cynthia L.;Kranz, Robert G.
通讯作者：
Kranz, Robert G.
影响因子：
9.8
作者：
Hu P;Janga SC;Babu M;Díaz-Mejía JJ;Butland G;Yang W;Pogoutse O;Guo X;Phanse S;Wong P;Chandran S;Christopoulos C;Nazarians-Armavil A;Nasseri NK;Musso G;Ali M;Nazemof N;Eroukova V;Golshani A;Paccanaro A;Greenblatt JF;Moreno-Hagelsieb G;Emili A
通讯作者：
Emili A