Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0027786 http://hdl.handle.net/11449/17709 |
Resumo: | Correlated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/alpha-lactalbumin family), we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation. |
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Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein FamiliesCorrelated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/alpha-lactalbumin family), we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Fed Minas Gerais, Inst Ciencias Biol, Dept Bioquim & Imunol, Belo Horizonte, MG, BrazilUniv Estadual Paulista, Dept Fis & Biofis, Botucatu, SP, BrazilUniv São Paulo, Inst Fis Sao Carlos, Dept Fis & Informat, Sao Carlos, SP, BrazilUniv Estadual Paulista, Dept Fis & Biofis, Botucatu, SP, BrazilFAPESP: 08/58734-1FAPESP: 98/14138-2Public Library ScienceUniversidade Federal de Minas Gerais (UFMG)Universidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Bleicher, LucasLemke, Ney [UNESP]Garratt, Richard Charles2014-05-20T13:49:40Z2014-05-20T13:49:40Z2011-12-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://dx.doi.org/10.1371/journal.pone.0027786Plos One. San Francisco: Public Library Science, v. 6, n. 12, p. 11, 2011.1932-6203http://hdl.handle.net/11449/1770910.1371/journal.pone.0027786WOS:000298666200001WOS000298666200001.pdf7977035910952141Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLOS ONE2.7661,164info:eu-repo/semantics/openAccess2023-12-01T06:12:45Zoai:repositorio.unesp.br:11449/17709Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-12-01T06:12:45Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
title |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
spellingShingle |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families Bleicher, Lucas |
title_short |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
title_full |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
title_fullStr |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
title_full_unstemmed |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
title_sort |
Using Amino Acid Correlation and Community Detection Algorithms to Identify Functional Determinants in Protein Families |
author |
Bleicher, Lucas |
author_facet |
Bleicher, Lucas Lemke, Ney [UNESP] Garratt, Richard Charles |
author_role |
author |
author2 |
Lemke, Ney [UNESP] Garratt, Richard Charles |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Minas Gerais (UFMG) Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Bleicher, Lucas Lemke, Ney [UNESP] Garratt, Richard Charles |
description |
Correlated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/alpha-lactalbumin family), we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12-20 2014-05-20T13:49:40Z 2014-05-20T13:49:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1371/journal.pone.0027786 Plos One. San Francisco: Public Library Science, v. 6, n. 12, p. 11, 2011. 1932-6203 http://hdl.handle.net/11449/17709 10.1371/journal.pone.0027786 WOS:000298666200001 WOS000298666200001.pdf 7977035910952141 |
url |
http://dx.doi.org/10.1371/journal.pone.0027786 http://hdl.handle.net/11449/17709 |
identifier_str_mv |
Plos One. San Francisco: Public Library Science, v. 6, n. 12, p. 11, 2011. 1932-6203 10.1371/journal.pone.0027786 WOS:000298666200001 WOS000298666200001.pdf 7977035910952141 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLOS ONE 2.766 1,164 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
11 application/pdf |
dc.publisher.none.fl_str_mv |
Public Library Science |
publisher.none.fl_str_mv |
Public Library Science |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965129425551360 |