A new method for the discovery of essential proteins.

<h4>Background</h4>Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of pr...

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Autores principales:	Xue Zhang, Jin Xu, Wang-xin Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13d47a8d9dee4e43931017d65c0f8f05
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spelling	oai:doaj.org-article:13d47a8d9dee4e43931017d65c0f8f052021-11-18T07:52:31ZA new method for the discovery of essential proteins.1932-620310.1371/journal.pone.0058763https://doaj.org/article/13d47a8d9dee4e43931017d65c0f8f052013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23555595/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value.<h4>Method</h4>In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality.<h4>Conclusions</h4>We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.Xue ZhangJin XuWang-xin XiaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58763 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xue Zhang Jin Xu Wang-xin Xiao A new method for the discovery of essential proteins.
description	<h4>Background</h4>Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value.<h4>Method</h4>In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality.<h4>Conclusions</h4>We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.
format	article
author	Xue Zhang Jin Xu Wang-xin Xiao
author_facet	Xue Zhang Jin Xu Wang-xin Xiao
author_sort	Xue Zhang
title	A new method for the discovery of essential proteins.
title_short	A new method for the discovery of essential proteins.
title_full	A new method for the discovery of essential proteins.
title_fullStr	A new method for the discovery of essential proteins.
title_full_unstemmed	A new method for the discovery of essential proteins.
title_sort	new method for the discovery of essential proteins.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/13d47a8d9dee4e43931017d65c0f8f05
work_keys_str_mv	AT xuezhang anewmethodforthediscoveryofessentialproteins AT jinxu anewmethodforthediscoveryofessentialproteins AT wangxinxiao anewmethodforthediscoveryofessentialproteins AT xuezhang newmethodforthediscoveryofessentialproteins AT jinxu newmethodforthediscoveryofessentialproteins AT wangxinxiao newmethodforthediscoveryofessentialproteins
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A new method for the discovery of essential proteins.

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