Protein interactions are important in a broad range of biological processes.
Traditionally, computational methods have been developed to automatically
predict protein interface from hand-crafted features. Recent approaches employ
deep neural networks and predict the interaction of each amino acid pair
independently. However, these methods do not incorporate the important
sequential information from amino acid chains and the high-order pairwise
interactions. Intuitively, the prediction of an amino acid pair should depend
on both their features and the information of other amino acid pairs. In this
work, we propose to formulate the protein interface prediction as a 2D dense
prediction problem. In addition, we propose a novel deep model to incorporate
the sequential information and high-order pairwise interactions to perform
interface predictions. We represent proteins as graphs and employ graph neural
networks to learn node features. Then we propose the sequential modeling method
to incorporate the sequential information and reorder the feature matrix. Next,
we incorporate high-order pairwise interactions to generate a 3D tensor
containing different pairwise interactions. Finally, we employ convolutional
neural networks to perform 2D dense predictions. Experimental results on
multiple benchmarks demonstrate that our proposed method can consistently
improve the protein interface prediction performance.

通过引入 2D dense prediction 方法和序列建模、配对交互等技术，我们提出了一种新的深度神经网络模型，能够通过图神经网络学习节点特征，并预测蛋白质界面的预测性能，并在实验中进行了验证并得出了可靠的结果。