Structural information about protein‐protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near‐native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics‐inspired deep learning framework. We represent protein and complex structures as intra‐ and inter‐molecular residue contact graphs with atom‐resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy‐based graph convolutional networks (EGCN) with multihead attention are trained to predict intra‐ and inter‐molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state‐of‐the‐art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community‐wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.

中文翻译：

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用于对蛋白质对接模型进行评分的基于能量的图卷积网络。

关于蛋白质-蛋白质相互作用的结构信息，通常在相互作用组尺度上缺失，对于理解细胞的机制和合理发现治疗方法很重要。蛋白质对接为此类信息提供了一种计算替代方案。然而，在大量候选者中对接近原生的对接模型进行高排名，通常被称为评分问题，仍然是一个关键的挑战。此外，估计模型质量，也称为质量评估问题，很少在蛋白质对接中得到解决。在这项研究中，蛋白质对接中的两个具有挑战性的问题分别被视为相对评分和绝对评分，并在一个受物理学启发的深度学习框架中解决。我们将蛋白质和复杂结构表示为具有原子分辨率节点和边缘特征的分子内和分子间残基接触图。我们提出了一种新颖的图卷积核，它通过边聚合交互节点的特征，以便可以直接从 3D 数据中学习广义交互能量。由此产生的具有多头注意力的基于能量的图卷积网络 (EGCN) 被训练来预测分子内和分子间的能量、结合亲和力和质量度量（界面 RMSD）。与用于模型排序的最先进的评分函数相比，EGCN 显着提高了涉及同源对接的预测交互（CAPRI）测试集的关键评估的排序；并且与 Score_set 相当或略好，由训练数据未知的各种社区范围的对接协议生成的 CAPRI 基准集。对于 Score_set 质量评估，EGCN 显示出比我们之前的努力提高了约 27%。EGCN 直接从图表示中的 3D 结构数据中学习，代表了用于蛋白质对接的图卷积网络的首次成功开发。