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RepTB: a gene ontology based drug repurposing approach for tuberculosis.
Passi, Anurag; Rajput, Neeraj Kumar; Wild, David J; Bhardwaj, Anshu.
Afiliação
  • Passi A; Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, 160036, India.
  • Rajput NK; Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, Training and Development Complex, CSIR Campus, CSIR Road, Taramani, Chennai, Tamil Nadu, 600113, India.
  • Wild DJ; Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, 160036, India.
  • Bhardwaj A; School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47405, USA. davidjwild@indiana.edu.
J Cheminform ; 10(1): 24, 2018 May 21.
Article em En | MEDLINE | ID: mdl-29785561
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 3_ND Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Cheminform Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 3_ND Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Cheminform Ano de publicação: 2018 Tipo de documento: Article