Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian

Abstract Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict effici...

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Autores principales:	Rulan Wang, Zhuo Wang, Hongfei Wang, Yuxuan Pang, Tzong-Yi Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a853eda2aeaf4504982c8c920cfc2d05
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spelling	oai:doaj.org-article:a853eda2aeaf4504982c8c920cfc2d052021-12-02T12:33:46ZCharacterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian10.1038/s41598-020-77173-02045-2322https://doaj.org/article/a853eda2aeaf4504982c8c920cfc2d052020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77173-0https://doaj.org/toc/2045-2322Abstract Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.Rulan WangZhuo WangHongfei WangYuxuan PangTzong-Yi LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rulan Wang Zhuo Wang Hongfei Wang Yuxuan Pang Tzong-Yi Lee Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
description	Abstract Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.
format	article
author	Rulan Wang Zhuo Wang Hongfei Wang Yuxuan Pang Tzong-Yi Lee
author_facet	Rulan Wang Zhuo Wang Hongfei Wang Yuxuan Pang Tzong-Yi Lee
author_sort	Rulan Wang
title	Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
title_short	Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
title_full	Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
title_fullStr	Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
title_full_unstemmed	Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
title_sort	characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a853eda2aeaf4504982c8c920cfc2d05
work_keys_str_mv	AT rulanwang characterizationandidentificationoflysinecrotonylationsitesbasedonmachinelearningmethodonbothplantandmammalian AT zhuowang characterizationandidentificationoflysinecrotonylationsitesbasedonmachinelearningmethodonbothplantandmammalian AT hongfeiwang characterizationandidentificationoflysinecrotonylationsitesbasedonmachinelearningmethodonbothplantandmammalian AT yuxuanpang characterizationandidentificationoflysinecrotonylationsitesbasedonmachinelearningmethodonbothplantandmammalian AT tzongyilee characterizationandidentificationoflysinecrotonylationsitesbasedonmachinelearningmethodonbothplantandmammalian
_version_	1718393877686124544

Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian

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