A new approach to the prediction of transmembrane structures

About 20%–30% of genome products have been predicted as membrane proteins, which have significant biological functions. The prediction of the amount and position for the transmembrane protein helical segments (TMHs) is the hot spot in bioinformatics. In this paper, a new approach, maximum spectrum o...

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Detalles Bibliográficos
Autores principales: Liu, HongDe, Wang, Rui, Lu, XiaoQuan, Chen, Jing, Liu, Xiuhui, Ding, Lan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SP Science in China Press 2008
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088861/
https://www.ncbi.nlm.nih.gov/pubmed/32214729
http://dx.doi.org/10.1007/s11434-008-0055-5
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author Liu, HongDe
Wang, Rui
Lu, XiaoQuan
Chen, Jing
Liu, Xiuhui
Ding, Lan
author_facet Liu, HongDe
Wang, Rui
Lu, XiaoQuan
Chen, Jing
Liu, Xiuhui
Ding, Lan
author_sort Liu, HongDe
collection PubMed
description About 20%–30% of genome products have been predicted as membrane proteins, which have significant biological functions. The prediction of the amount and position for the transmembrane protein helical segments (TMHs) is the hot spot in bioinformatics. In this paper, a new approach, maximum spectrum of continuous wavelet transform (MSCWT), is proposed to predict TMHs. The predictions for eight SARS-CoV membrane proteins indicate that MSCWT has the same capacity with software TMpred. Moreover, the test on a dataset of 131 structure-known proteins with 548 TMHs shows that the prediction accuracy of MSCWT for TMHs is 91.6% and that for membrane protein is 89.3%.
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spelling pubmed-70888612020-03-23 A new approach to the prediction of transmembrane structures Liu, HongDe Wang, Rui Lu, XiaoQuan Chen, Jing Liu, Xiuhui Ding, Lan Chin Sci Bull Articles About 20%–30% of genome products have been predicted as membrane proteins, which have significant biological functions. The prediction of the amount and position for the transmembrane protein helical segments (TMHs) is the hot spot in bioinformatics. In this paper, a new approach, maximum spectrum of continuous wavelet transform (MSCWT), is proposed to predict TMHs. The predictions for eight SARS-CoV membrane proteins indicate that MSCWT has the same capacity with software TMpred. Moreover, the test on a dataset of 131 structure-known proteins with 548 TMHs shows that the prediction accuracy of MSCWT for TMHs is 91.6% and that for membrane protein is 89.3%. SP Science in China Press 2008-05-23 2008 /pmc/articles/PMC7088861/ /pubmed/32214729 http://dx.doi.org/10.1007/s11434-008-0055-5 Text en © Science in China Press 2008 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Articles
Liu, HongDe
Wang, Rui
Lu, XiaoQuan
Chen, Jing
Liu, Xiuhui
Ding, Lan
A new approach to the prediction of transmembrane structures
title A new approach to the prediction of transmembrane structures
title_full A new approach to the prediction of transmembrane structures
title_fullStr A new approach to the prediction of transmembrane structures
title_full_unstemmed A new approach to the prediction of transmembrane structures
title_short A new approach to the prediction of transmembrane structures
title_sort new approach to the prediction of transmembrane structures
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088861/
https://www.ncbi.nlm.nih.gov/pubmed/32214729
http://dx.doi.org/10.1007/s11434-008-0055-5
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