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Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework
Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194944/ https://www.ncbi.nlm.nih.gov/pubmed/35712582 http://dx.doi.org/10.3389/fpls.2022.912599 |
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author | Chen, Yifan Li, Zejun Li, Zhiyong |
author_facet | Chen, Yifan Li, Zejun Li, Zhiyong |
author_sort | Chen, Yifan |
collection | PubMed |
description | Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins. |
format | Online Article Text |
id | pubmed-9194944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91949442022-06-15 Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework Chen, Yifan Li, Zejun Li, Zhiyong Front Plant Sci Plant Science Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins. Frontiers Media S.A. 2022-05-31 /pmc/articles/PMC9194944/ /pubmed/35712582 http://dx.doi.org/10.3389/fpls.2022.912599 Text en Copyright © 2022 Chen, Li and Li. 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 | Plant Science Chen, Yifan Li, Zejun Li, Zhiyong Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title | Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title_full | Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title_fullStr | Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title_full_unstemmed | Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title_short | Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework |
title_sort | prediction of plant resistance proteins based on pairwise energy content and stacking framework |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194944/ https://www.ncbi.nlm.nih.gov/pubmed/35712582 http://dx.doi.org/10.3389/fpls.2022.912599 |
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