论文标题
图形神经网络的预训练，用于建模突变对蛋白质 - 蛋白质结合亲和力的影响
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity
论文作者
论文摘要
建模突变对结合亲和力的影响在蛋白质工程和药物设计中起着至关重要的作用。在这项研究中，我们开发了一个新型的基于深度学习的框架，称为GraphPpi，以根据图神经网络（GNN）提供的特征来预测突变的结合亲和力变化。特别是，GraphPPI首先采用了精心设计的预训练方案来强制执行GNN，以捕获以无监督的方式预测突变对结合亲和力的影响的特征，然后将这些图形特征与梯度增强树集成在一起，以执行预测。实验表明，没有任何带注释的信号，GraphPpi可以捕获有意义的蛋白质结构模式。同样，GraphPpi在预测五个基准数据集上单点突变和多点突变的结合亲和力变化方面还取得了新的最新性能。深度分析还表明，GraphPpi可以准确估计突变对SARS-COV-2及其中和抗体之间结合亲和力的影响。这些结果已将GraphPpi确立为蛋白质设计研究中强大而有用的计算工具。
Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design. In this study, we develop a novel deep learning based framework, named GraphPPI, to predict the binding affinity changes upon mutations based on the features provided by a graph neural network (GNN). In particular, GraphPPI first employs a well-designed pre-training scheme to enforce the GNN to capture the features that are predictive of the effects of mutations on binding affinity in an unsupervised manner and then integrates these graphical features with gradient-boosting trees to perform the prediction. Experiments showed that, without any annotated signals, GraphPPI can capture meaningful patterns of the protein structures. Also, GraphPPI achieved new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on five benchmark datasets. In-depth analyses also showed GraphPPI can accurately estimate the effects of mutations on the binding affinity between SARS-CoV-2 and its neutralizing antibodies. These results have established GraphPPI as a powerful and useful computational tool in the studies of protein design.
