Deep learning approaches achieved significant progress in predicting protein
structures. These methods are often applied to protein-protein interactions
(PPIs) yet require Multiple Sequence Alignment (MSA) which is unavailable for
various interactions, such as antibody-antigen. Computational docking methods
are capable of sampling accurate complex models, but also produce thousands of
invalid configurations. The design of scoring functions for identifying
accurate models is a long-standing challenge. We develop a novel
attention-based Graph Neural Network (GNN), ContactNet, for classifying PPI
models obtained from docking algorithms into accurate and incorrect ones. When
trained on docked antigen and modeled antibody structures, ContactNet doubles
the accuracy of current state-of-the-art scoring functions, achieving accurate
models among its Top-10 at 43% of the test cases. When applied to unbound
antibodies, its Top-10 accuracy increases to 65%. This performance is achieved
without MSA and the approach is applicable to other types of interactions, such
as host-pathogens or general PPIs.

我们开发了一种基于注意力的图神经网络 (ContactNet)，用于将从对接算法中得到的蛋白质相互作用模型分类为准确和错误的模型，不需要多序列比对，适用于其他类型的相互作用。