A systematic identification of species-specific protein succinylation sites using joint element features information

Md Mehedi Hasan,1 Mst Shamima Khatun,2 Md Nurul Haque Mollah,2 Cao Yong,3 Dianjing Guo1 1School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong, People’s Republic of China; 2Laboratory of Bioinformat...

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Autores principales:	Hasan MM, Khatun MS, Mollah MNH, Yong C, Guo D
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2017
Materias:	Post-translation modification Succinylation sites prediction Machine learning Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/8fee99395dda47fdbe8549c8d5dcb7e2
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spelling	oai:doaj.org-article:8fee99395dda47fdbe8549c8d5dcb7e22021-12-02T07:46:15ZA systematic identification of species-specific protein succinylation sites using joint element features information1178-2013https://doaj.org/article/8fee99395dda47fdbe8549c8d5dcb7e22017-08-01T00:00:00Zhttps://www.dovepress.com/a-systematic-identification-of-species-specific-protein-succinylation--peer-reviewed-article-IJNhttps://doaj.org/toc/1178-2013Md Mehedi Hasan,1 Mst Shamima Khatun,2 Md Nurul Haque Mollah,2 Cao Yong,3 Dianjing Guo1 1School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong, People’s Republic of China; 2Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh; 3Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, People’s Republic of China Abstract: Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible. Keywords: posttranslation modification, succinylation site prediction, machine learning, sequence encoding, feature selectionHasan MMKhatun MSMollah MNHYong CGuo DDove Medical PressarticlePost-translation modificationSuccinylation sites predictionMachine learningMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol Volume 12, Pp 6303-6315 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Post-translation modification Succinylation sites prediction Machine learning Medicine (General) R5-920
spellingShingle	Post-translation modification Succinylation sites prediction Machine learning Medicine (General) R5-920 Hasan MM Khatun MS Mollah MNH Yong C Guo D A systematic identification of species-specific protein succinylation sites using joint element features information
description	Md Mehedi Hasan,1 Mst Shamima Khatun,2 Md Nurul Haque Mollah,2 Cao Yong,3 Dianjing Guo1 1School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territory, Hong Kong, People’s Republic of China; 2Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh; 3Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, People’s Republic of China Abstract: Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible. Keywords: posttranslation modification, succinylation site prediction, machine learning, sequence encoding, feature selection
format	article
author	Hasan MM Khatun MS Mollah MNH Yong C Guo D
author_facet	Hasan MM Khatun MS Mollah MNH Yong C Guo D
author_sort	Hasan MM
title	A systematic identification of species-specific protein succinylation sites using joint element features information
title_short	A systematic identification of species-specific protein succinylation sites using joint element features information
title_full	A systematic identification of species-specific protein succinylation sites using joint element features information
title_fullStr	A systematic identification of species-specific protein succinylation sites using joint element features information
title_full_unstemmed	A systematic identification of species-specific protein succinylation sites using joint element features information
title_sort	systematic identification of species-specific protein succinylation sites using joint element features information
publisher	Dove Medical Press
publishDate	2017
url	https://doaj.org/article/8fee99395dda47fdbe8549c8d5dcb7e2
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A systematic identification of species-specific protein succinylation sites using joint element features information

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