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RepTB: a gene ontology based drug repurposing approach for tuberculosis

Tuberculosis (TB) is the world’s leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we...

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Autores principales: Passi, Anurag, Rajput, Neeraj Kumar, Wild, David J., Bhardwaj, Anshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962481/
https://www.ncbi.nlm.nih.gov/pubmed/29785561
http://dx.doi.org/10.1186/s13321-018-0276-9
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author Passi, Anurag
Rajput, Neeraj Kumar
Wild, David J.
Bhardwaj, Anshu
author_facet Passi, Anurag
Rajput, Neeraj Kumar
Wild, David J.
Bhardwaj, Anshu
author_sort Passi, Anurag
collection PubMed
description Tuberculosis (TB) is the world’s leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study, we have created a Gene Ontology based network containing 26,404 edges, 6630 drug and 4083 target nodes. The network, enriched with molecular function ontology, was analyzed using Network Based Inference (NBI). The association scores computed from NBI are used to identify novel drug-target interactions. These interactions are further evaluated based on a combined evidence approach for identification of potential drug repurposing candidates. In this approach, targets which have no known variation in clinical isolates, no human homologs, and are essential for Mtb’s survival and or virulence are prioritized. We analyzed predicted DTIs to identify target pairs whose predicted drugs may have synergistic bactericidal effect. From the list of predicted DTIs from RepTB, four TB targets, namely, FolP1 (Dihydropteroate synthase), Tmk (Thymidylate kinase), Dut (Deoxyuridine 5′-triphosphate nucleotidohydrolase) and MenB (1,4-dihydroxy-2-naphthoyl-CoA synthase) may be selected for further validation. In addition, we observed that in some cases there is significant chemical structure similarity between predicted and reported drugs of prioritized targets, lending credence to our approach. We also report new chemical space for prioritized targets that may be tested further. We believe that with increasing drug-target interaction dataset RepTB will be able to offer better predictive value and is amenable for identification of drug-repurposing candidates for other disease indications too. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0276-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-59624812018-07-06 RepTB: a gene ontology based drug repurposing approach for tuberculosis Passi, Anurag Rajput, Neeraj Kumar Wild, David J. Bhardwaj, Anshu J Cheminform Research Article Tuberculosis (TB) is the world’s leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study, we have created a Gene Ontology based network containing 26,404 edges, 6630 drug and 4083 target nodes. The network, enriched with molecular function ontology, was analyzed using Network Based Inference (NBI). The association scores computed from NBI are used to identify novel drug-target interactions. These interactions are further evaluated based on a combined evidence approach for identification of potential drug repurposing candidates. In this approach, targets which have no known variation in clinical isolates, no human homologs, and are essential for Mtb’s survival and or virulence are prioritized. We analyzed predicted DTIs to identify target pairs whose predicted drugs may have synergistic bactericidal effect. From the list of predicted DTIs from RepTB, four TB targets, namely, FolP1 (Dihydropteroate synthase), Tmk (Thymidylate kinase), Dut (Deoxyuridine 5′-triphosphate nucleotidohydrolase) and MenB (1,4-dihydroxy-2-naphthoyl-CoA synthase) may be selected for further validation. In addition, we observed that in some cases there is significant chemical structure similarity between predicted and reported drugs of prioritized targets, lending credence to our approach. We also report new chemical space for prioritized targets that may be tested further. We believe that with increasing drug-target interaction dataset RepTB will be able to offer better predictive value and is amenable for identification of drug-repurposing candidates for other disease indications too. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0276-9) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-05-21 /pmc/articles/PMC5962481/ /pubmed/29785561 http://dx.doi.org/10.1186/s13321-018-0276-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Passi, Anurag
Rajput, Neeraj Kumar
Wild, David J.
Bhardwaj, Anshu
RepTB: a gene ontology based drug repurposing approach for tuberculosis
title RepTB: a gene ontology based drug repurposing approach for tuberculosis
title_full RepTB: a gene ontology based drug repurposing approach for tuberculosis
title_fullStr RepTB: a gene ontology based drug repurposing approach for tuberculosis
title_full_unstemmed RepTB: a gene ontology based drug repurposing approach for tuberculosis
title_short RepTB: a gene ontology based drug repurposing approach for tuberculosis
title_sort reptb: a gene ontology based drug repurposing approach for tuberculosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962481/
https://www.ncbi.nlm.nih.gov/pubmed/29785561
http://dx.doi.org/10.1186/s13321-018-0276-9
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