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Ekins S, Godbole AA, Kéri G, Orfi L, Pato J, Bhat RS, Verma R, Bradley EK, Nagaraja V. Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I. Tuberculosis (Edinb) 2017; 103:52-60. [PMID: 28237034 DOI: 10.1016/j.tube.2017.01.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 01/14/2017] [Accepted: 01/18/2017] [Indexed: 11/30/2022]
Abstract
There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94403, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
| | - Adwait Anand Godbole
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | - György Kéri
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary; Semmelweis Univ, Dept Med Chem, MTA SE Pathobiochem Res Grp, H-1092, Budapest, Hungary
| | - Lászlo Orfi
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary; Semmelweis Univ, Dept Med Chem, MTA SE Pathobiochem Res Grp, H-1092, Budapest, Hungary
| | - János Pato
- Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary
| | - Rajeshwari Subray Bhat
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | - Rinkee Verma
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India
| | | | - Valakunja Nagaraja
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India; Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, 560064, India.
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