Transmembrane protein alignment and fold recognition based on predicted topology.

<h4>Background</h4>Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from s...

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Autores principales:	Han Wang, Zhiquan He, Chao Zhang, Li Zhang, Dong Xu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c285231c589c43bda813adfd8f76e082
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spelling	oai:doaj.org-article:c285231c589c43bda813adfd8f76e0822021-11-18T09:03:45ZTransmembrane protein alignment and fold recognition based on predicted topology.1932-620310.1371/journal.pone.0069744https://doaj.org/article/c285231c589c43bda813adfd8f76e0822013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23894534/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.<h4>Methodology</h4>We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.<h4>Results</h4>We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.Han WangZhiquan HeChao ZhangLi ZhangDong XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69744 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Han Wang Zhiquan He Chao Zhang Li Zhang Dong Xu Transmembrane protein alignment and fold recognition based on predicted topology.
description	<h4>Background</h4>Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.<h4>Methodology</h4>We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.<h4>Results</h4>We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.
format	article
author	Han Wang Zhiquan He Chao Zhang Li Zhang Dong Xu
author_facet	Han Wang Zhiquan He Chao Zhang Li Zhang Dong Xu
author_sort	Han Wang
title	Transmembrane protein alignment and fold recognition based on predicted topology.
title_short	Transmembrane protein alignment and fold recognition based on predicted topology.
title_full	Transmembrane protein alignment and fold recognition based on predicted topology.
title_fullStr	Transmembrane protein alignment and fold recognition based on predicted topology.
title_full_unstemmed	Transmembrane protein alignment and fold recognition based on predicted topology.
title_sort	transmembrane protein alignment and fold recognition based on predicted topology.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c285231c589c43bda813adfd8f76e082
work_keys_str_mv	AT hanwang transmembraneproteinalignmentandfoldrecognitionbasedonpredictedtopology AT zhiquanhe transmembraneproteinalignmentandfoldrecognitionbasedonpredictedtopology AT chaozhang transmembraneproteinalignmentandfoldrecognitionbasedonpredictedtopology AT lizhang transmembraneproteinalignmentandfoldrecognitionbasedonpredictedtopology AT dongxu transmembraneproteinalignmentandfoldrecognitionbasedonpredictedtopology
_version_	1718420989241458688

Transmembrane protein alignment and fold recognition based on predicted topology.

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