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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...

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Detalles Bibliográficos
Autores principales: Chen, Yifan, Li, Zejun, Li, Zhiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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