Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purpos...

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Autores principales:	Ioannis A. Tamposis, Dimitra Sarantopoulou, Margarita C. Theodoropoulou, Evangelia A. Stasi, Panagiota I. Kontou, Konstantinos D. Tsirigos, Pantelis G. Bagos
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Hidden Markov Models Hidden Neural Networks Membrane proteins Sequence analysis Neural Networks Protein structure prediction Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/1826dcfedd9741849193e7818f992144
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spelling	oai:doaj.org-article:1826dcfedd9741849193e7818f9921442021-11-20T05:05:23ZHidden neural networks for transmembrane protein topology prediction2001-037010.1016/j.csbj.2021.11.006https://doaj.org/article/1826dcfedd9741849193e7818f9921442021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004712https://doaj.org/toc/2001-0370Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.Ioannis A. TamposisDimitra SarantopoulouMargarita C. TheodoropoulouEvangelia A. StasiPanagiota I. KontouKonstantinos D. TsirigosPantelis G. BagosElsevierarticleHidden Markov ModelsHidden Neural NetworksMembrane proteinsSequence analysisNeural NetworksProtein structure predictionBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6090-6097 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hidden Markov Models Hidden Neural Networks Membrane proteins Sequence analysis Neural Networks Protein structure prediction Biotechnology TP248.13-248.65
spellingShingle	Hidden Markov Models Hidden Neural Networks Membrane proteins Sequence analysis Neural Networks Protein structure prediction Biotechnology TP248.13-248.65 Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos Hidden neural networks for transmembrane protein topology prediction
description	Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.
format	article
author	Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos
author_facet	Ioannis A. Tamposis Dimitra Sarantopoulou Margarita C. Theodoropoulou Evangelia A. Stasi Panagiota I. Kontou Konstantinos D. Tsirigos Pantelis G. Bagos
author_sort	Ioannis A. Tamposis
title	Hidden neural networks for transmembrane protein topology prediction
title_short	Hidden neural networks for transmembrane protein topology prediction
title_full	Hidden neural networks for transmembrane protein topology prediction
title_fullStr	Hidden neural networks for transmembrane protein topology prediction
title_full_unstemmed	Hidden neural networks for transmembrane protein topology prediction
title_sort	hidden neural networks for transmembrane protein topology prediction
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/1826dcfedd9741849193e7818f992144
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_version_	1718419584305856512

Hidden neural networks for transmembrane protein topology prediction

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