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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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Detalles Bibliográficos
Autores principales: Dobson, Laszlo, Tusnády, Gábor E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623522/
https://www.ncbi.nlm.nih.gov/pubmed/34830151
http://dx.doi.org/10.3390/ijms222212270
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author Dobson, Laszlo
Tusnády, Gábor E.
author_facet Dobson, Laszlo
Tusnády, Gábor E.
author_sort Dobson, Laszlo
collection PubMed
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.
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spelling pubmed-86235222021-11-27 MemDis: Predicting Disordered Regions in Transmembrane Proteins Dobson, Laszlo Tusnády, Gábor E. Int J Mol Sci Communication 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. MDPI 2021-11-12 /pmc/articles/PMC8623522/ /pubmed/34830151 http://dx.doi.org/10.3390/ijms222212270 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Dobson, Laszlo
Tusnády, Gábor E.
MemDis: Predicting Disordered Regions in Transmembrane Proteins
title 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_short MemDis: Predicting Disordered Regions in Transmembrane Proteins
title_sort memdis: predicting disordered regions in transmembrane proteins
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623522/
https://www.ncbi.nlm.nih.gov/pubmed/34830151
http://dx.doi.org/10.3390/ijms222212270
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