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A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites
The identification of protein–protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing metho...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528219/ https://www.ncbi.nlm.nih.gov/pubmed/37771850 http://dx.doi.org/10.34133/research.0240 |
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author | Mou, Minjie Pan, Ziqi Zhou, Zhimeng Zheng, Lingyan Zhang, Hanyu Shi, Shuiyang Li, Fengcheng Sun, Xiuna Zhu, Feng |
author_facet | Mou, Minjie Pan, Ziqi Zhou, Zhimeng Zheng, Lingyan Zhang, Hanyu Shi, Shuiyang Li, Fengcheng Sun, Xiuna Zhu, Feng |
author_sort | Mou, Minjie |
collection | PubMed |
description | The identification of protein–protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis. |
format | Online Article Text |
id | pubmed-10528219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-105282192023-09-28 A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites Mou, Minjie Pan, Ziqi Zhou, Zhimeng Zheng, Lingyan Zhang, Hanyu Shi, Shuiyang Li, Fengcheng Sun, Xiuna Zhu, Feng Research (Wash D C) Research Article The identification of protein–protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis. AAAS 2023-09-27 /pmc/articles/PMC10528219/ /pubmed/37771850 http://dx.doi.org/10.34133/research.0240 Text en Copyright © 2023 Minjie Mou et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Mou, Minjie Pan, Ziqi Zhou, Zhimeng Zheng, Lingyan Zhang, Hanyu Shi, Shuiyang Li, Fengcheng Sun, Xiuna Zhu, Feng A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title | A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title_full | A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title_fullStr | A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title_full_unstemmed | A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title_short | A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites |
title_sort | transformer-based ensemble framework for the prediction of protein–protein interaction sites |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528219/ https://www.ncbi.nlm.nih.gov/pubmed/37771850 http://dx.doi.org/10.34133/research.0240 |
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