The Development of a Universal In Silico Predictor of Protein-Protein Interactions
Autor(a) principal: | |
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Data de Publicação: | 2013 |
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.0065587 http://hdl.handle.net/11449/75468 |
Resumo: | Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al. |
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The Development of a Universal In Silico Predictor of Protein-Protein Interactionsamino acidasparaginecysteineisoleucineamino acid sequenceclassificationdecision treemachine learningpredictionprotein protein interactionstatistical analysisstatistical modeluniversal in silico predictor of protein protein interactionProtein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.Department of Morphology Universidade Estadual Paulista (UNESP), Botucatu, Sao PauloDepartment of Physics and Biophysics Universidade Estadual Paulista (UNESP), Botucatu, Sao PauloDepartment of Morphology Universidade Estadual Paulista (UNESP), Botucatu, Sao PauloDepartment of Physics and Biophysics Universidade Estadual Paulista (UNESP), Botucatu, Sao PauloUniversidade Estadual Paulista (Unesp)Valente, Guilherme T. [UNESP]Acencio, Marcio L. [UNESP]Martins, Cesar [UNESP]Lemke, Ney [UNESP]2014-05-27T11:29:33Z2014-05-27T11:29:33Z2013-05-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1371/journal.pone.0065587PLoS ONE, v. 8, n. 5, 2013.1932-6203http://hdl.handle.net/11449/7546810.1371/journal.pone.0065587WOS:0003197999002122-s2.0-848785830332-s2.0-84878583033.pdf885880069942535279770359109521410000-0003-3534-974XScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLOS ONE2.7661,164info:eu-repo/semantics/openAccess2023-12-17T06:16:34Zoai:repositorio.unesp.br:11449/75468Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-12-17T06:16:34Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
title |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
spellingShingle |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions Valente, Guilherme T. [UNESP] amino acid asparagine cysteine isoleucine amino acid sequence classification decision tree machine learning prediction protein protein interaction statistical analysis statistical model universal in silico predictor of protein protein interaction |
title_short |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
title_full |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
title_fullStr |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
title_full_unstemmed |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
title_sort |
The Development of a Universal In Silico Predictor of Protein-Protein Interactions |
author |
Valente, Guilherme T. [UNESP] |
author_facet |
Valente, Guilherme T. [UNESP] Acencio, Marcio L. [UNESP] Martins, Cesar [UNESP] Lemke, Ney [UNESP] |
author_role |
author |
author2 |
Acencio, Marcio L. [UNESP] Martins, Cesar [UNESP] Lemke, Ney [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Valente, Guilherme T. [UNESP] Acencio, Marcio L. [UNESP] Martins, Cesar [UNESP] Lemke, Ney [UNESP] |
dc.subject.por.fl_str_mv |
amino acid asparagine cysteine isoleucine amino acid sequence classification decision tree machine learning prediction protein protein interaction statistical analysis statistical model universal in silico predictor of protein protein interaction |
topic |
amino acid asparagine cysteine isoleucine amino acid sequence classification decision tree machine learning prediction protein protein interaction statistical analysis statistical model universal in silico predictor of protein protein interaction |
description |
Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-05-31 2014-05-27T11:29:33Z 2014-05-27T11:29:33Z |
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.0065587 PLoS ONE, v. 8, n. 5, 2013. 1932-6203 http://hdl.handle.net/11449/75468 10.1371/journal.pone.0065587 WOS:000319799900212 2-s2.0-84878583033 2-s2.0-84878583033.pdf 8858800699425352 7977035910952141 0000-0003-3534-974X |
url |
http://dx.doi.org/10.1371/journal.pone.0065587 http://hdl.handle.net/11449/75468 |
identifier_str_mv |
PLoS ONE, v. 8, n. 5, 2013. 1932-6203 10.1371/journal.pone.0065587 WOS:000319799900212 2-s2.0-84878583033 2-s2.0-84878583033.pdf 8858800699425352 7977035910952141 0000-0003-3534-974X |
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 |
application/pdf |
dc.source.none.fl_str_mv |
Scopus 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 |
|
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1799965312123142144 |