Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of the target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.

中文翻译：

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通过使用与GCN链接的CNN进行顺序和空间邻居信息来进行对位预测

由可变区和恒定区组成的抗体是一种特殊类型的蛋白质，在脊椎动物的免疫系统中起着至关重要的作用。它们具有非凡的亲和力和特异性结合多种多样抗原的卓越能力。这种结合的延展性使抗体成为一类重要的生物药物和生物标志物。在本文中，我们提出了一种基于序列和结构特征来鉴定抗体的哪些氨基酸残基直接与其相关抗原相互作用的方法。我们的算法使用与图卷积网络（GCN）链接的卷积神经网络（CNN）来利用来自顺序邻居和空间邻居的信息，以了解有关目标氨基酸残基的局部环境的更多信息。此外，我们通过使用注意力层来处理抗体的抗原伴侣。我们的方法改进了最新的方法。