Cargando…
PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning
MOTIVATION: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protei...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384406/ https://www.ncbi.nlm.nih.gov/pubmed/34185052 http://dx.doi.org/10.1093/bioinformatics/btab480 |
_version_ | 1783741909185855488 |
---|---|
author | Dobson, Laszlo Zeke, András Tusnády, Gábor E |
author_facet | Dobson, Laszlo Zeke, András Tusnády, Gábor E |
author_sort | Dobson, Laszlo |
collection | PubMed |
description | MOTIVATION: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protein trafficking to these domains, although the exact rules governing them are still elusive. RESULTS: In this study we prepared neural networks that capture recurrent patterns to classify transmembrane proteins localizing into apical and basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided method may offer help to experimentalists to identify or better characterize molecular networks regulating the distribution of transporters or surface receptors (including viral entry receptors like that of COVID-19). AVAILABILITY AND IMPLEMENTATION: The prediction server PolarProtPred is available at http://polarprotpred.ttk.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8384406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83844062021-09-01 PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning Dobson, Laszlo Zeke, András Tusnády, Gábor E Bioinformatics Original Papers MOTIVATION: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protein trafficking to these domains, although the exact rules governing them are still elusive. RESULTS: In this study we prepared neural networks that capture recurrent patterns to classify transmembrane proteins localizing into apical and basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided method may offer help to experimentalists to identify or better characterize molecular networks regulating the distribution of transporters or surface receptors (including viral entry receptors like that of COVID-19). AVAILABILITY AND IMPLEMENTATION: The prediction server PolarProtPred is available at http://polarprotpred.ttk.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-06-29 /pmc/articles/PMC8384406/ /pubmed/34185052 http://dx.doi.org/10.1093/bioinformatics/btab480 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Dobson, Laszlo Zeke, András Tusnády, Gábor E PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title | PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title_full | PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title_fullStr | PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title_full_unstemmed | PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title_short | PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
title_sort | polarprotpred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384406/ https://www.ncbi.nlm.nih.gov/pubmed/34185052 http://dx.doi.org/10.1093/bioinformatics/btab480 |
work_keys_str_mv | AT dobsonlaszlo polarprotpredpredictingapicalandbasolaterallocalizationoftransmembraneproteinsusingputativeshortlinearmotifsanddeeplearning AT zekeandras polarprotpredpredictingapicalandbasolaterallocalizationoftransmembraneproteinsusingputativeshortlinearmotifsanddeeplearning AT tusnadygabore polarprotpredpredictingapicalandbasolaterallocalizationoftransmembraneproteinsusingputativeshortlinearmotifsanddeeplearning |