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 und...

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Autores principales:	Minli Tang, Longxin Wu, Xinyu Yu, Zhaoqi Chu, Shuting Jin, Juan Liu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	protein–protein interaction multilevel attention mechanism feature fusion deep learning protein features Genetics QH426-470
Acceso en línea:	https://doaj.org/article/3f7a123c230141ca98a97e6b6ea59ee7
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spelling	oai:doaj.org-article:3f7a123c230141ca98a97e6b6ea59ee72021-11-30T09:57:28ZPrediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms1664-802110.3389/fgene.2021.784863https://doaj.org/article/3f7a123c230141ca98a97e6b6ea59ee72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.784863/fullhttps://doaj.org/toc/1664-8021Proteins 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.Minli TangMinli TangLongxin WuXinyu YuZhaoqi ChuShuting JinShuting JinJuan LiuFrontiers Media S.A.articleprotein–protein interactionmultilevel attention mechanismfeature fusiondeep learningprotein featuresGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein–protein interaction multilevel attention mechanism feature fusion deep learning protein features Genetics QH426-470
spellingShingle	protein–protein interaction multilevel attention mechanism feature fusion deep learning protein features Genetics QH426-470 Minli Tang Minli Tang Longxin Wu Xinyu Yu Zhaoqi Chu Shuting Jin Shuting Jin Juan Liu Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
description	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.
format	article
author	Minli Tang Minli Tang Longxin Wu Xinyu Yu Zhaoqi Chu Shuting Jin Shuting Jin Juan Liu
author_facet	Minli Tang Minli Tang Longxin Wu Xinyu Yu Zhaoqi Chu Shuting Jin Shuting Jin Juan Liu
author_sort	Minli Tang
title	Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
title_short	Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
title_full	Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
title_fullStr	Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
title_full_unstemmed	Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms
title_sort	prediction of protein–protein interaction sites based on stratified attentional mechanisms
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/3f7a123c230141ca98a97e6b6ea59ee7
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_version_	1718406677368143872

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

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