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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8623522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT dobsonlaszlo memdispredictingdisorderedregionsintransmembraneproteins AT tusnadygabore memdispredictingdisorderedregionsintransmembraneproteins |