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Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework

Authors :
Yifan Chen
Zejun Li
Zhiyong Li
Source :
Frontiers in Plant Science, Vol 13 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

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.

Details

Language :
English
ISSN :
1664462X and 42586046
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Plant Science
Publication Type :
Academic Journal
Accession number :
edsdoj.b55e8f425860463181bd9b5b9ca63fb9
Document Type :
article
Full Text :
https://doi.org/10.3389/fpls.2022.912599