Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/120519 |
Resumo: | Background: Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D-structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Results: Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D-structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal's descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D-structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. Conclusions: The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches. © 2017 The Author(s). |
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Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zoneAlignmentBacteriaBenchmarkingClassification (of information)Encoding (symbols)EnzymesSupport vector machinesTopologyAntibacterial peptidesComputational predictionsDescriptorsPost-translational modificationsProtDCalProtein analysisSequence based featuresTI2BioPProteinsBackground: Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D-structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Results: Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D-structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal's descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D-structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. Conclusions: The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches. © 2017 The Author(s).BMC20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/120519eng1471210510.1186/s12859-017-1758-xRuiz-Blanco Y.B.Agüero-Chapin G.García-Hernández E.Álvarez O.Antunes A.Green J.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:33:59Zoai:repositorio-aberto.up.pt:10216/120519Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:42:46.174922Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
title |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
spellingShingle |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone Ruiz-Blanco Y.B. Alignment Bacteria Benchmarking Classification (of information) Encoding (symbols) Enzymes Support vector machines Topology Antibacterial peptides Computational predictions Descriptors Post-translational modifications ProtDCal Protein analysis Sequence based features TI2BioP Proteins |
title_short |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
title_full |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
title_fullStr |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
title_full_unstemmed |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
title_sort |
Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone |
author |
Ruiz-Blanco Y.B. |
author_facet |
Ruiz-Blanco Y.B. Agüero-Chapin G. García-Hernández E. Álvarez O. Antunes A. Green J. |
author_role |
author |
author2 |
Agüero-Chapin G. García-Hernández E. Álvarez O. Antunes A. Green J. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Ruiz-Blanco Y.B. Agüero-Chapin G. García-Hernández E. Álvarez O. Antunes A. Green J. |
dc.subject.por.fl_str_mv |
Alignment Bacteria Benchmarking Classification (of information) Encoding (symbols) Enzymes Support vector machines Topology Antibacterial peptides Computational predictions Descriptors Post-translational modifications ProtDCal Protein analysis Sequence based features TI2BioP Proteins |
topic |
Alignment Bacteria Benchmarking Classification (of information) Encoding (symbols) Enzymes Support vector machines Topology Antibacterial peptides Computational predictions Descriptors Post-translational modifications ProtDCal Protein analysis Sequence based features TI2BioP Proteins |
description |
Background: Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D-structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Results: Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D-structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal's descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D-structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. Conclusions: The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches. © 2017 The Author(s). |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z |
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 |
https://hdl.handle.net/10216/120519 |
url |
https://hdl.handle.net/10216/120519 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
14712105 10.1186/s12859-017-1758-x |
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.publisher.none.fl_str_mv |
BMC |
publisher.none.fl_str_mv |
BMC |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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