Prediction of Protein-Protein Interaction Sites Based on Stratified Attentional Mechanisms

Proteins are the basic substances that undertake human life activities, and they often perform their biological functions through interactions with other biological macromolecules, such as cell transmission and signal transduction. Predicting the interaction sites between proteins can deepen the understanding of the principle of protein interactions, but traditional experimental methods are time-consuming and labor-intensive. In this study, a new hierarchical attention network structure, named HANPPIS, by adding six effective features of protein sequence, position-specific scoring matrix (PSSM), secondary structure, pre-training vector, hydrophilic, and amino acid position, is proposed to predict protein-protein interaction (PPI) sites. The experiment proved that our model has obtained very effective results, which was better than the existing advanced calculation methods. More importantly, we used the double-layer attention mechanism to improve the interpretability of the model and to a certain extent solved the problem of the black box of deep neural networks, which can be used as a reference for location positioning on the biological level.

Automated summary

A new HANPPIS model was proposed in this study to predict protein-protein interaction sites by adding six protein sequence features, which proved to be effective and superior. Additionally, the use of a double-layer attention mechanism improved the interpretability of the model and solved the black box problem of deep neural networks.