Deep Learning of High-Order Interactions for Protein Interface Prediction

TL;DR

This paper introduces a deep learning framework that models high-order interactions and sequential information for protein interface prediction, significantly improving accuracy over previous methods.

Contribution

It formulates protein interface prediction as a 2D dense prediction task and develops a novel deep model combining graph neural networks, sequential modeling, and high-order interaction tensors.

Findings

Consistently outperforms existing methods on multiple benchmarks.

Effectively incorporates sequential and high-order interaction information.

Enhances prediction accuracy for protein interfaces.

Abstract

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.