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Development of a prediction system for tail-anchored proteins
BACKGROUND: “Tail-anchored (TA) proteins” is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3–5 % of all transmembrane proteins that mediate membrane fusion, regulation of apop...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025589/ https://www.ncbi.nlm.nih.gov/pubmed/27634135 http://dx.doi.org/10.1186/s12859-016-1202-7 |
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author | Shigemitsu, Shunsuke Cao, Wei Terada, Tohru Shimizu, Kentaro |
author_facet | Shigemitsu, Shunsuke Cao, Wei Terada, Tohru Shimizu, Kentaro |
author_sort | Shigemitsu, Shunsuke |
collection | PubMed |
description | BACKGROUND: “Tail-anchored (TA) proteins” is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3–5 % of all transmembrane proteins that mediate membrane fusion, regulation of apoptosis, and vesicular transport. The combined use of TMD and signal sequence prediction tools is typically required to predict TA proteins. RESULTS: Here we developed a prediction system named TAPPM that predicted TA proteins solely from target amino acid sequences according to the knowledge of the sequence features of TMDs and the peripheral regions of TA proteins. Manually curated TA proteins were collected from published literature. We constructed hidden markov models of TA proteins as well as three different types of transmembrane proteins with similar structures and compared their likelihoods as TA proteins. CONCLUSIONS: Using the HMM models, we achieved high prediction accuracy; area under the receiver operator curve values reaching 0.963. A command line tool written in Python is available at https://github.com/davecao/tappm_cli. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1202-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5025589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50255892016-09-20 Development of a prediction system for tail-anchored proteins Shigemitsu, Shunsuke Cao, Wei Terada, Tohru Shimizu, Kentaro BMC Bioinformatics Research Article BACKGROUND: “Tail-anchored (TA) proteins” is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3–5 % of all transmembrane proteins that mediate membrane fusion, regulation of apoptosis, and vesicular transport. The combined use of TMD and signal sequence prediction tools is typically required to predict TA proteins. RESULTS: Here we developed a prediction system named TAPPM that predicted TA proteins solely from target amino acid sequences according to the knowledge of the sequence features of TMDs and the peripheral regions of TA proteins. Manually curated TA proteins were collected from published literature. We constructed hidden markov models of TA proteins as well as three different types of transmembrane proteins with similar structures and compared their likelihoods as TA proteins. CONCLUSIONS: Using the HMM models, we achieved high prediction accuracy; area under the receiver operator curve values reaching 0.963. A command line tool written in Python is available at https://github.com/davecao/tappm_cli. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1202-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-15 /pmc/articles/PMC5025589/ /pubmed/27634135 http://dx.doi.org/10.1186/s12859-016-1202-7 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Shigemitsu, Shunsuke Cao, Wei Terada, Tohru Shimizu, Kentaro Development of a prediction system for tail-anchored proteins |
title | Development of a prediction system for tail-anchored proteins |
title_full | Development of a prediction system for tail-anchored proteins |
title_fullStr | Development of a prediction system for tail-anchored proteins |
title_full_unstemmed | Development of a prediction system for tail-anchored proteins |
title_short | Development of a prediction system for tail-anchored proteins |
title_sort | development of a prediction system for tail-anchored proteins |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025589/ https://www.ncbi.nlm.nih.gov/pubmed/27634135 http://dx.doi.org/10.1186/s12859-016-1202-7 |
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