Protein docking model evaluation by 3D deep convolutional neural networks
Protein docking model evaluation by 3D deep convolutional neural networks
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DOI：
10.1093/bioinformatics/btz870
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发表时间：
2020-04-01
期刊：
影响因子：
5.8
通讯作者：
Kihara, Daisuke
中科院分区：
文献类型：
--
作者：
Wang, Xiao;Terashi, Genki;Kihara, Daisuke
动机：许多重要的细胞过程涉及蛋白质的物理相互作用。因此，确定蛋白质四级结构为理解复合物功能的分子机制提供了重要的见解。为了补充实验方法，许多计算方法已经被开发来预测蛋白质复合物的结构。计算蛋白质复合物结构预测的挑战之一是从大量生成的models.Results池中识别近原生模型：我们开发了一种基于卷积深度神经网络的方法，称为DOCKing诱饵选择与基于体素的深度神经网络（DOVE）用于评估蛋白质对接模型。为了评估蛋白质对接模型，DOVE用3D体素扫描模型的蛋白质-蛋白质界面，并将原子相互作用类型及其能量贡献作为应用于神经网络的输入特征。深度学习模型在ZDock和DockGround数据库中可用的对接模型上进行了训练和验证。在测试的不同功能组合中，几乎所有功能都优于现有的评分功能。
Motivation: Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models.Results: We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein-protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions.