Detecting protein candidate fragments using a structural alphabet profile comparison approach.

Predicting accurate fragments from sequence has recently become a critical step for protein structure modeling, as protein fragment assembly techniques are presently among the most efficient approaches for de novo prediction. A key step in these approaches is, given the sequence of a protein to mode...

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Autores principales:	Yimin Shen, Géraldine Picord, Frédéric Guyon, Pierre Tuffery
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/41d9e69324de4bb6826fde0b54640da4
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id	oai:doaj.org-article:41d9e69324de4bb6826fde0b54640da4
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spelling	oai:doaj.org-article:41d9e69324de4bb6826fde0b54640da42021-11-18T08:44:37ZDetecting protein candidate fragments using a structural alphabet profile comparison approach.1932-620310.1371/journal.pone.0080493https://doaj.org/article/41d9e69324de4bb6826fde0b54640da42013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24303019/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Predicting accurate fragments from sequence has recently become a critical step for protein structure modeling, as protein fragment assembly techniques are presently among the most efficient approaches for de novo prediction. A key step in these approaches is, given the sequence of a protein to model, the identification of relevant fragments - candidate fragments - from a collection of the available 3D structures. These fragments can then be assembled to produce a model of the complete structure of the protein of interest. The search for candidate fragments is classically achieved by considering local sequence similarity using profile comparison, or threading approaches. In the present study, we introduce a new profile comparison approach that, instead of using amino acid profiles, is based on the use of predicted structural alphabet profiles, where structural alphabet profiles contain information related to the 3D local shapes associated with the sequences. We show that structural alphabet profile-profile comparison can be used efficiently to retrieve accurate structural fragments, and we introduce a fully new protocol for the detection of candidate fragments. It identifies fragments specific of each position of the sequence and of size varying between 6 and 27 amino-acids. We find it outperforms present state of the art approaches in terms (i) of the accuracy of the fragments identified, (ii) the rate of true positives identified, while having a high coverage score. We illustrate the relevance of the approach on complete target sets of the two previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 9 and 10. A web server for the approach is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/SAFrag.Yimin ShenGéraldine PicordFrédéric GuyonPierre TufferyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e80493 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yimin Shen Géraldine Picord Frédéric Guyon Pierre Tuffery Detecting protein candidate fragments using a structural alphabet profile comparison approach.
description	Predicting accurate fragments from sequence has recently become a critical step for protein structure modeling, as protein fragment assembly techniques are presently among the most efficient approaches for de novo prediction. A key step in these approaches is, given the sequence of a protein to model, the identification of relevant fragments - candidate fragments - from a collection of the available 3D structures. These fragments can then be assembled to produce a model of the complete structure of the protein of interest. The search for candidate fragments is classically achieved by considering local sequence similarity using profile comparison, or threading approaches. In the present study, we introduce a new profile comparison approach that, instead of using amino acid profiles, is based on the use of predicted structural alphabet profiles, where structural alphabet profiles contain information related to the 3D local shapes associated with the sequences. We show that structural alphabet profile-profile comparison can be used efficiently to retrieve accurate structural fragments, and we introduce a fully new protocol for the detection of candidate fragments. It identifies fragments specific of each position of the sequence and of size varying between 6 and 27 amino-acids. We find it outperforms present state of the art approaches in terms (i) of the accuracy of the fragments identified, (ii) the rate of true positives identified, while having a high coverage score. We illustrate the relevance of the approach on complete target sets of the two previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 9 and 10. A web server for the approach is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/SAFrag.
format	article
author	Yimin Shen Géraldine Picord Frédéric Guyon Pierre Tuffery
author_facet	Yimin Shen Géraldine Picord Frédéric Guyon Pierre Tuffery
author_sort	Yimin Shen
title	Detecting protein candidate fragments using a structural alphabet profile comparison approach.
title_short	Detecting protein candidate fragments using a structural alphabet profile comparison approach.
title_full	Detecting protein candidate fragments using a structural alphabet profile comparison approach.
title_fullStr	Detecting protein candidate fragments using a structural alphabet profile comparison approach.
title_full_unstemmed	Detecting protein candidate fragments using a structural alphabet profile comparison approach.
title_sort	detecting protein candidate fragments using a structural alphabet profile comparison approach.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/41d9e69324de4bb6826fde0b54640da4
work_keys_str_mv	AT yiminshen detectingproteincandidatefragmentsusingastructuralalphabetprofilecomparisonapproach AT geraldinepicord detectingproteincandidatefragmentsusingastructuralalphabetprofilecomparisonapproach AT fredericguyon detectingproteincandidatefragmentsusingastructuralalphabetprofilecomparisonapproach AT pierretuffery detectingproteincandidatefragmentsusingastructuralalphabetprofilecomparisonapproach
_version_	1718421378880765952

Detecting protein candidate fragments using a structural alphabet profile comparison approach.

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