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Article

Knowledge Based Identification of Potent Antitubercular Compounds Using Structure Based Virtual Screening and Structure Interaction Fingerprints

Ashutosh Kumar^†, Vinita Chaturvedi^‡, Shalini Bhatnagar^‡, Sudhir Sinha^‡ and Mohammad Imran Siddiqi*^†

Molecular and Structural Biology Division and Drug Target Discovery and Development Division, Central Drug Research Institute, Lucknow 226001, India

J. Chem. Inf. Model., 2009, 49 (1), pp 35–42

DOI: 10.1021/ci8003607

Publication Date (Web): December 9, 2008

†

Molecular and Structural Biology Division.

‡

Drug Target Discovery and Development Division.

* Corresponding author e-mail: imsiddiqi@yahoo.com.

Abstract

In view of the worldwide spread of multidrug resistance of Mycobacterium tuberculosis, there is an urgent need to discover antitubercular agents with novel structures. Thymidine monophosphate kinase from M. tuberculosis (TMPKmt) is an attractive target for antitubercular chemotherapy. We report here the identification of potent antitubercular compounds targeting TMPKmt using virtual screening methods. For this purpose we have developed a pharmacophore hypothesis based on the substrate and known TMPKmt inhibitors and employed it to screen the Maybridge small molecule database. The molecular docking was then performed in order to select the compounds on the basis of their ability to form favorable interactions with the TMPKmt active site. In addition, we applied straightforward weighting using structure interaction fingerprints to include additional knowledge into structure based virtual screening. Eight compounds were acquired and evaluated for antitubercular activity against M. tuberculosis H37Rv in vitro, and out of these 3 compounds showed MIC of 3.12 μg/mL whereas 2 compounds showed MIC of 12.5 μg/mL. All the active compounds were found to be nontoxic in Vero cell lines and mice bone marrow macrophages. All the identified hits highlighted a key hydrogen bonding interaction with Arg74. The observed π-stacking interaction with Phe70 was also produced by the identified hits. These hits represent promising starting points for structural optimization in hit-to-lead development.

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