{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T03:24:03Z","timestamp":1771817043744,"version":"3.50.1"},"reference-count":110,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Chem."],"abstract":"<jats:p>Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of <jats:italic>Mycobacterium tuberculosis<\/jats:italic> (<jats:italic>Mtb<\/jats:italic>), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant <jats:italic>Mtb<\/jats:italic> which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.<\/jats:p>","DOI":"10.3389\/fchem.2020.593497","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T05:05:49Z","timestamp":1607922349000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis"],"prefix":"10.3389","volume":"8","author":[{"given":"Ushashi","family":"Banerjee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santhosh","family":"Sankar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nagasuma","family":"Chandra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e10","DOI":"10.1038\/emi.2016.10","article-title":"Updates on the risk factors for latent tuberculosis reactivation and their managements","volume":"5","author":"Ai","year":"2016","journal-title":"Emerg. 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