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Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS
Prediction of protein-protein interaction (PPI) sites is one of the most perplexing problems in drug discovery and computational biology. Although significant progress has been made by combining different machine learning techniques with a variety of distinct characteristics, the problem still remai...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553539/ https://www.ncbi.nlm.nih.gov/pubmed/36246558 http://dx.doi.org/10.1155/2022/5892627 |
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author | Zhang, JunYan Lyu, Yinghua Ma, Zhiqiang |
author_facet | Zhang, JunYan Lyu, Yinghua Ma, Zhiqiang |
author_sort | Zhang, JunYan |
collection | PubMed |
description | Prediction of protein-protein interaction (PPI) sites is one of the most perplexing problems in drug discovery and computational biology. Although significant progress has been made by combining different machine learning techniques with a variety of distinct characteristics, the problem still remains unresolved. In this study, a technique for PPI sites is presented using a random forest (RF) algorithm followed by the minimum redundancy maximal relevance (mRMR) approach, and the method of incremental feature selection (IFS). Physicochemical properties of proteins and the features of the residual disorder, sequence conservation, secondary structure, and solvent accessibility are incorporated. Five 3D structural characteristics are also used to predict PPI sites. Analysis of features shows that 3D structural features such as relative solvent-accessible surface area (RASA) and surface curvature (SC) help in the prediction of PPI sites. Results show that the performance of the proposed predictor is superior to several other state-of-the-art predictors, whose average prediction accuracy is 81.44%, sensitivity is 82.17%, and specificity is 80.71%, respectively. The proposed predictor is expected to become a helpful tool for finding PPI sites, and the feature analysis presented in this study will give useful insights into protein interaction mechanisms. |
format | Online Article Text |
id | pubmed-9553539 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95535392022-10-13 Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS Zhang, JunYan Lyu, Yinghua Ma, Zhiqiang Dis Markers Research Article Prediction of protein-protein interaction (PPI) sites is one of the most perplexing problems in drug discovery and computational biology. Although significant progress has been made by combining different machine learning techniques with a variety of distinct characteristics, the problem still remains unresolved. In this study, a technique for PPI sites is presented using a random forest (RF) algorithm followed by the minimum redundancy maximal relevance (mRMR) approach, and the method of incremental feature selection (IFS). Physicochemical properties of proteins and the features of the residual disorder, sequence conservation, secondary structure, and solvent accessibility are incorporated. Five 3D structural characteristics are also used to predict PPI sites. Analysis of features shows that 3D structural features such as relative solvent-accessible surface area (RASA) and surface curvature (SC) help in the prediction of PPI sites. Results show that the performance of the proposed predictor is superior to several other state-of-the-art predictors, whose average prediction accuracy is 81.44%, sensitivity is 82.17%, and specificity is 80.71%, respectively. The proposed predictor is expected to become a helpful tool for finding PPI sites, and the feature analysis presented in this study will give useful insights into protein interaction mechanisms. Hindawi 2022-10-04 /pmc/articles/PMC9553539/ /pubmed/36246558 http://dx.doi.org/10.1155/2022/5892627 Text en Copyright © 2022 JunYan Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, JunYan Lyu, Yinghua Ma, Zhiqiang Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title | Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title_full | Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title_fullStr | Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title_full_unstemmed | Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title_short | Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS |
title_sort | prediction of protein-protein interaction sites by multifeature fusion and rf with mrmr and ifs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553539/ https://www.ncbi.nlm.nih.gov/pubmed/36246558 http://dx.doi.org/10.1155/2022/5892627 |
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