Predicting S-nitrosylation proteins and sites by fusing multiple features

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for bot...

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Autores principales:	Wang-Ren Qiu, Qian-Kun Wang, Meng-Yue Guan, Jian-Hua Jia, Xuan Xiao
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	s-nitrosylation random forest post-translational modification multiple features identification Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8125e5cdbe6d4b8e9153fb38707c867a
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spelling	oai:doaj.org-article:8125e5cdbe6d4b8e9153fb38707c867a2021-11-29T05:40:51ZPredicting S-nitrosylation proteins and sites by fusing multiple features10.3934/mbe.20214501551-0018https://doaj.org/article/8125e5cdbe6d4b8e9153fb38707c867a2021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021450?viewType=HTMLhttps://doaj.org/toc/1551-0018Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of k-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPSWang-Ren Qiu Qian-Kun Wang Meng-Yue GuanJian-Hua JiaXuan XiaoAIMS Pressarticles-nitrosylationrandom forestpost-translational modificationmultiple featuresidentificationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9132-9147 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	s-nitrosylation random forest post-translational modification multiple features identification Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	s-nitrosylation random forest post-translational modification multiple features identification Biotechnology TP248.13-248.65 Mathematics QA1-939 Wang-Ren Qiu Qian-Kun Wang Meng-Yue Guan Jian-Hua Jia Xuan Xiao Predicting S-nitrosylation proteins and sites by fusing multiple features
description	Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of k-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS
format	article
author	Wang-Ren Qiu Qian-Kun Wang Meng-Yue Guan Jian-Hua Jia Xuan Xiao
author_facet	Wang-Ren Qiu Qian-Kun Wang Meng-Yue Guan Jian-Hua Jia Xuan Xiao
author_sort	Wang-Ren Qiu
title	Predicting S-nitrosylation proteins and sites by fusing multiple features
title_short	Predicting S-nitrosylation proteins and sites by fusing multiple features
title_full	Predicting S-nitrosylation proteins and sites by fusing multiple features
title_fullStr	Predicting S-nitrosylation proteins and sites by fusing multiple features
title_full_unstemmed	Predicting S-nitrosylation proteins and sites by fusing multiple features
title_sort	predicting s-nitrosylation proteins and sites by fusing multiple features
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/8125e5cdbe6d4b8e9153fb38707c867a
work_keys_str_mv	AT wangrenqiu predictingsnitrosylationproteinsandsitesbyfusingmultiplefeatures AT qiankunwang predictingsnitrosylationproteinsandsitesbyfusingmultiplefeatures AT mengyueguan predictingsnitrosylationproteinsandsitesbyfusingmultiplefeatures AT jianhuajia predictingsnitrosylationproteinsandsitesbyfusingmultiplefeatures AT xuanxiao predictingsnitrosylationproteinsandsitesbyfusingmultiplefeatures
_version_	1718407654733250560

Predicting S-nitrosylation proteins and sites by fusing multiple features

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