Abstract
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.
Highlights
Proteins participate in various biological processes in organisms
We propose a deep learning framework HANPPIS to predict protein interaction sites at the amino acid level
The difference between HANPPIS and other existing methods is that the model uses hierarchical attention combined with neural networks to predict protein interaction sites
Summary
Proteins participate in various biological processes in organisms. They usually do not play a single role but interact with other biological macromolecules to perform biological functions (Geng et al, 2015). The identification of protein interaction sites can help researchers understand how proteins perform their biological functions (Ofran and Rost, 2003; Nilofer et al, 2020), and it can help design new antibacterial drugs (Gainza et al, 2020). Biological experimental methods have disadvantages of being expensive and time-consuming. It is of great value for biologists to develop accurate calculation methods to predict protein interaction sites
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