Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection
ABSTRACT: Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease...
- Autores:
-
Flórez Amaya, Andrés Felipe
Park, Daeui
Bhak, Jong
Kim, Byoung-Chul
Kuchinsky, Allan
Morris, John H
Espinosa, Jairo
Muskus López, Carlos Enrique
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2010
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/25750
- Acceso en línea:
- http://hdl.handle.net/10495/25750
- Palabra clave:
- Leishmania
Leishmaniasis
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by/2.5/co/
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Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| title |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| spellingShingle |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection Leishmania Leishmaniasis |
| title_short |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| title_full |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| title_fullStr |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| title_full_unstemmed |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| title_sort |
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection |
| dc.creator.fl_str_mv |
Flórez Amaya, Andrés Felipe Park, Daeui Bhak, Jong Kim, Byoung-Chul Kuchinsky, Allan Morris, John H Espinosa, Jairo Muskus López, Carlos Enrique |
| dc.contributor.author.none.fl_str_mv |
Flórez Amaya, Andrés Felipe Park, Daeui Bhak, Jong Kim, Byoung-Chul Kuchinsky, Allan Morris, John H Espinosa, Jairo Muskus López, Carlos Enrique |
| dc.contributor.researchgroup.spa.fl_str_mv |
Programa de Estudio y Control de Enfermedades Tropicales (PECET) |
| dc.subject.decs.none.fl_str_mv |
Leishmania Leishmaniasis |
| topic |
Leishmania Leishmaniasis |
| description |
ABSTRACT: Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets. Conclusion We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources. |
| publishDate |
2010 |
| dc.date.issued.none.fl_str_mv |
2010 |
| dc.date.accessioned.none.fl_str_mv |
2022-02-02T17:21:31Z |
| dc.date.available.none.fl_str_mv |
2022-02-02T17:21:31Z |
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Artículo de investigación |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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http://hdl.handle.net/10495/25750 |
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1471-2105 |
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http://hdl.handle.net/10495/25750 |
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1471-2105 |
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eng |
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eng |
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BMC Bioinformatics. |
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9 |
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1 |
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11 |
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BMC Bioinformatics |
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http://creativecommons.org/licenses/by/2.5/co/ |
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openAccess |
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application/pdf |
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Londres, Inglaterra |
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Flórez Amaya, Andrés FelipePark, DaeuiBhak, JongKim, Byoung-ChulKuchinsky, AllanMorris, John HEspinosa, JairoMuskus López, Carlos EnriquePrograma de Estudio y Control de Enfermedades Tropicales (PECET)2022-02-02T17:21:31Z2022-02-02T17:21:31Z2010http://hdl.handle.net/10495/257501471-2105ABSTRACT: Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets. Conclusion We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.COL00150999application/pdfengBMCLondres, Inglaterrahttp://creativecommons.org/licenses/by/2.5/co/https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Protein network prediction and topological analysis in Leishmania major as a tool for drug target selectionArtículo de investigaciónhttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleLeishmaniaLeishmaniasisBMC Bioinformatics.9111BMC BioinformaticsPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://bibliotecadigital.udea.edu.co/bitstreams/45bb4375-91e7-4838-a9c6-9d2858306d57/download8a4605be74aa9ea9d79846c1fba20a33MD53falseAnonymousREADORIGINALFlorezAndres_2010_ProteinNetworkPrediction.pdfFlorezAndres_2010_ProteinNetworkPrediction.pdfArtículo de investigaciónapplication/pdf1300707https://bibliotecadigital.udea.edu.co/bitstreams/6948d247-2f4b-41ee-927a-0d697df89f2e/download3ca72e8658ea6563f20e51fc975b6e1cMD51trueAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8927https://bibliotecadigital.udea.edu.co/bitstreams/23a450ce-77fa-49c4-8075-00b4baa6c776/download1646d1f6b96dbbbc38035efc9239ac9cMD52falseAnonymousREADTEXTFlorezAndres_2010_ProteinNetworkPrediction.pdf.txtFlorezAndres_2010_ProteinNetworkPrediction.pdf.txtExtracted texttext/plain46737https://bibliotecadigital.udea.edu.co/bitstreams/a8e206e2-2221-40b0-ad6e-575fa784475c/downloadcadb2f530844653d4ab39590c1a28892MD54falseAnonymousREADTHUMBNAILFlorezAndres_2010_ProteinNetworkPrediction.pdf.jpgFlorezAndres_2010_ProteinNetworkPrediction.pdf.jpgGenerated Thumbnailimage/jpeg15787https://bibliotecadigital.udea.edu.co/bitstreams/1c0a3e29-40d9-4d48-8639-b44fbc368b0a/downloadbf664256bbc3557bf36d719424e9064aMD55falseAnonymousREAD10495/25750oai:bibliotecadigital.udea.edu.co:10495/257502025-03-27 00:22:46.701http://creativecommons.org/licenses/by/2.5/co/open.accesshttps://bibliotecadigital.udea.edu.coRepositorio Institucional de la Universidad de Antioquiaaplicacionbibliotecadigitalbiblioteca@udea.edu.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 |
