MemDis: Predicting Disordered Regions in Transmembrane Proteins

Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of predict...

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Autores principales:	Laszlo Dobson, Gábor E. Tusnády
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	transmembrane proteins intrinsically disordered proteins deep learning convolutional neural network bidirectional long-short term memory Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/b7ed5da9901a42c4a36f8f95b406679a
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id	oai:doaj.org-article:b7ed5da9901a42c4a36f8f95b406679a
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spelling	oai:doaj.org-article:b7ed5da9901a42c4a36f8f95b406679a2021-11-25T17:54:54ZMemDis: Predicting Disordered Regions in Transmembrane Proteins10.3390/ijms2222122701422-00671661-6596https://doaj.org/article/b7ed5da9901a42c4a36f8f95b406679a2021-11-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/22/12270https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.Laszlo DobsonGábor E. TusnádyMDPI AGarticletransmembrane proteinsintrinsically disordered proteinsdeep learningconvolutional neural networkbidirectional long-short term memoryBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 12270, p 12270 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	transmembrane proteins intrinsically disordered proteins deep learning convolutional neural network bidirectional long-short term memory Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	transmembrane proteins intrinsically disordered proteins deep learning convolutional neural network bidirectional long-short term memory Biology (General) QH301-705.5 Chemistry QD1-999 Laszlo Dobson Gábor E. Tusnády MemDis: Predicting Disordered Regions in Transmembrane Proteins
description	Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.
format	article
author	Laszlo Dobson Gábor E. Tusnády
author_facet	Laszlo Dobson Gábor E. Tusnády
author_sort	Laszlo Dobson
title	MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_short	MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_full	MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_fullStr	MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_full_unstemmed	MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_sort	memdis: predicting disordered regions in transmembrane proteins
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b7ed5da9901a42c4a36f8f95b406679a
work_keys_str_mv	AT laszlodobson memdispredictingdisorderedregionsintransmembraneproteins AT gaboretusnady memdispredictingdisorderedregionsintransmembraneproteins
_version_	1718411883245993984

MemDis: Predicting Disordered Regions in Transmembrane Proteins

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