Abstract：
Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch.
摘要：
抗体是一类通过结合病原体的抗原来识别和中和病原体的蛋白质。它们是用于诊断和治疗应用的最重要的生物制药类别。了解抗体如何与其抗原相互作用在药物和疫苗设计中起着基本作用，并有助于包含复杂的抗原结合机制。由于实验方法的总体成本，预测抗体-抗原相互作用位点的计算方法具有重要价值。机器学习方法和深度学习技术取得了有希望的成果。在这项工作中,我们通过应用HSS-PPI预测抗体相互作用界面位点，一种用于预测一般蛋白质界面位点的混合方法。该方法以分层表示的方式抽象蛋白质，并使用图卷积网络对界面和非界面之间的氨基酸进行分类。此外，我们还为氨基酸配备了不同的物理化学特征和结构来描述残基。分析结果，我们观察到结构特征在氨基酸描述中起着基本作用。我们比较了获得的性能，使用标准指标进行评估，使用具有3DZernike描述符的SVM获得的，Parapred,Paratome,和抗体i-补丁。
