Prediction of multi-type membrane proteins in human by an integrated approach.

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computa...

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Autores principales:	Guohua Huang, Yuchao Zhang, Lei Chen, Ning Zhang, Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9726ca5e02cb410e80378bad6da115e8
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spelling	oai:doaj.org-article:9726ca5e02cb410e80378bad6da115e82021-11-18T08:25:47ZPrediction of multi-type membrane proteins in human by an integrated approach.1932-620310.1371/journal.pone.0093553https://doaj.org/article/9726ca5e02cb410e80378bad6da115e82014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24676214/?tool=EBIhttps://doaj.org/toc/1932-6203Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.Guohua HuangYuchao ZhangLei ChenNing ZhangTao HuangYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e93553 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guohua Huang Yuchao Zhang Lei Chen Ning Zhang Tao Huang Yu-Dong Cai Prediction of multi-type membrane proteins in human by an integrated approach.
description	Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.
format	article
author	Guohua Huang Yuchao Zhang Lei Chen Ning Zhang Tao Huang Yu-Dong Cai
author_facet	Guohua Huang Yuchao Zhang Lei Chen Ning Zhang Tao Huang Yu-Dong Cai
author_sort	Guohua Huang
title	Prediction of multi-type membrane proteins in human by an integrated approach.
title_short	Prediction of multi-type membrane proteins in human by an integrated approach.
title_full	Prediction of multi-type membrane proteins in human by an integrated approach.
title_fullStr	Prediction of multi-type membrane proteins in human by an integrated approach.
title_full_unstemmed	Prediction of multi-type membrane proteins in human by an integrated approach.
title_sort	prediction of multi-type membrane proteins in human by an integrated approach.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/9726ca5e02cb410e80378bad6da115e8
work_keys_str_mv	AT guohuahuang predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach AT yuchaozhang predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach AT leichen predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach AT ningzhang predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach AT taohuang predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach AT yudongcai predictionofmultitypemembraneproteinsinhumanbyanintegratedapproach
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Prediction of multi-type membrane proteins in human by an integrated approach.

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