Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources

Abstract Most proteins undergo different kinds of modification after translation. Protein acetylation is one of the most crucial post-translational modifications, which causes direct or indirect impact on various biological activities in vivo. As a member of Class III HDACs, SIRT1 was the closest on...

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Autores principales:	Zichao Zhai, Ming Tang, Yue Yang, Ming Lu, Wei-Guo Zhu, Tingting Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a3d988050944d63864709f0ccbcd90a
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spelling	oai:doaj.org-article:7a3d988050944d63864709f0ccbcd90a2021-12-02T12:32:58ZIdentifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources10.1038/s41598-017-04847-72045-2322https://doaj.org/article/7a3d988050944d63864709f0ccbcd90a2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04847-7https://doaj.org/toc/2045-2322Abstract Most proteins undergo different kinds of modification after translation. Protein acetylation is one of the most crucial post-translational modifications, which causes direct or indirect impact on various biological activities in vivo. As a member of Class III HDACs, SIRT1 was the closest one to the yeast sir2 and drew most attention, while a small number of known SIRT1 substrates caused difficulties to clarify its function. In this work, we designed a novel computational method to screen SIRT1 substrates based on manually collected data and Support Vector Machines (SVMs). Unlike other approaches, we took both primary sequence and protein functional features into consideration. Through integrating functional features, the Matthews correlation coefficient (MCC) for the prediction increased from 0.10 to 0.65. The prediction results were verified by independent dataset and biological experiments. The validation results demostrated that our classifier could effectively identify SIRT1 substrates and filter appropriate candidates for further research. Furthermore, we provide online tool to support SIRT1 substrates prediction, which is freely available at http://bioinfo.bjmu.edu.cn/huac/ .Zichao ZhaiMing TangYue YangMing LuWei-Guo ZhuTingting LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zichao Zhai Ming Tang Yue Yang Ming Lu Wei-Guo Zhu Tingting Li Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
description	Abstract Most proteins undergo different kinds of modification after translation. Protein acetylation is one of the most crucial post-translational modifications, which causes direct or indirect impact on various biological activities in vivo. As a member of Class III HDACs, SIRT1 was the closest one to the yeast sir2 and drew most attention, while a small number of known SIRT1 substrates caused difficulties to clarify its function. In this work, we designed a novel computational method to screen SIRT1 substrates based on manually collected data and Support Vector Machines (SVMs). Unlike other approaches, we took both primary sequence and protein functional features into consideration. Through integrating functional features, the Matthews correlation coefficient (MCC) for the prediction increased from 0.10 to 0.65. The prediction results were verified by independent dataset and biological experiments. The validation results demostrated that our classifier could effectively identify SIRT1 substrates and filter appropriate candidates for further research. Furthermore, we provide online tool to support SIRT1 substrates prediction, which is freely available at http://bioinfo.bjmu.edu.cn/huac/ .
format	article
author	Zichao Zhai Ming Tang Yue Yang Ming Lu Wei-Guo Zhu Tingting Li
author_facet	Zichao Zhai Ming Tang Yue Yang Ming Lu Wei-Guo Zhu Tingting Li
author_sort	Zichao Zhai
title	Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
title_short	Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
title_full	Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
title_fullStr	Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
title_full_unstemmed	Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources
title_sort	identifying human sirt1 substrates by integrating heterogeneous information from various sources
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/7a3d988050944d63864709f0ccbcd90a
work_keys_str_mv	AT zichaozhai identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources AT mingtang identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources AT yueyang identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources AT minglu identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources AT weiguozhu identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources AT tingtingli identifyinghumansirt1substratesbyintegratingheterogeneousinformationfromvarioussources
_version_	1718393926979682304

Identifying Human SIRT1 Substrates by Integrating Heterogeneous Information from Various Sources

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