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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647646/ https://www.ncbi.nlm.nih.gov/pubmed/34880910 http://dx.doi.org/10.3389/fgene.2021.784863 |
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author | Tang, Minli Wu, Longxin Yu, Xinyu Chu, Zhaoqi Jin, Shuting Liu, Juan |
author_facet | Tang, Minli Wu, Longxin Yu, Xinyu Chu, Zhaoqi Jin, Shuting Liu, Juan |
author_sort | Tang, Minli |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8647646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86476462021-12-07 Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms Tang, Minli Wu, Longxin Yu, Xinyu Chu, Zhaoqi Jin, Shuting Liu, Juan Front Genet Genetics 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. Frontiers Media S.A. 2021-11-22 /pmc/articles/PMC8647646/ /pubmed/34880910 http://dx.doi.org/10.3389/fgene.2021.784863 Text en Copyright © 2021 Tang, Wu, Yu, Chu, Jin and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Tang, Minli Wu, Longxin Yu, Xinyu Chu, Zhaoqi Jin, Shuting Liu, Juan Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms |
title | 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_short | Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms |
title_sort | prediction of protein–protein interaction sites based on stratified attentional mechanisms |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647646/ https://www.ncbi.nlm.nih.gov/pubmed/34880910 http://dx.doi.org/10.3389/fgene.2021.784863 |
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