Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I

• Published in: Tuberculosis (Edinburgh, Scotland) Vol. 103; pp. 52 - 60
• Main Authors: Ekins, Sean, Godbole, Adwait Anand, Kéri, György, Orfi, Lászlo, Pato, János, Bhat, Rajeshwari Subray, Verma, Rinkee, Bradley, Erin K, Nagaraja, Valakunja
• Format: Journal Article
• Language: English
• Published: Scotland Elsevier Ltd 01.03.2017; Elsevier Science Ltd
• Subjects: Antitubercular Agents - chemistry; Antitubercular Agents - metabolism; Antitubercular Agents - pharmacology; Artificial intelligence; Bayes Theorem; Bayesian analysis; Bayesian models; Collaborative drug discovery tuberculosis database; Computer applications; Computer-Aided Design; Crystal structure; DNA topoisomerase; DNA Topoisomerases, Type I - chemistry; DNA Topoisomerases, Type I - metabolism; Docking; Dose-Response Relationship, Drug; Drug Design; Drugs; Function class fingerprints; Homology; Homology model; Humans; Imipramine; In vitro methods and tests; Infectious Disease; Inhibition; Inhibitors; Learning algorithms; Machine Learning; Mathematical models; Molecular Docking Simulation; Molecular Targeted Therapy; Mycobacterium smegmatis - drug effects; Mycobacterium smegmatis - enzymology; Mycobacterium smegmatis - growth & development; Mycobacterium tuberculosis; Mycobacterium tuberculosis - drug effects; Mycobacterium tuberculosis - enzymology; Mycobacterium tuberculosis - growth & development; Protein Conformation; Pulmonary/Respiratory; Structure-Activity Relationship; Topoisomerase; Topoisomerase I Inhibitors - chemistry; Topoisomerase I Inhibitors - metabolism; Topoisomerase I Inhibitors - pharmacology; Tuberculosis; Tuberculosis - drug therapy; Tuberculosis - microbiology; Bayesian models; Tuberculosis; Mycobacterium tuberculosis; Homology model; Topoisomerase; Docking; Collaborative drug discovery tuberculosis database; Function class fingerprints
• ISSN: 1472-9792; 1873-281X
• DOI: 10.1016/j.tube.2017.01.005

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.