title: Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.
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.
authors.affiliation: Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94403, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA. Electronic address: ekinssean@yahoo.com.
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
authors.affiliation: Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India.
authors.forename: Adwait Anand
authors.initials: AA
authors.lastname: Godbole
authors.affiliation: 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.
authors.forename: György
authors.initials: G
authors.lastname: Kéri
authors.affiliation: 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.
authors.forename: Lászlo
authors.initials: L
authors.lastname: Orfi
authors.affiliation: Vichem Chemie Research Ltd., Herman Ottó u. 15, H-1022, Budapest, Hungary.
authors.forename: János
authors.initials: J
authors.lastname: Pato
authors.affiliation: Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India.
authors.forename: Rajeshwari Subray
authors.initials: RS
authors.lastname: Bhat
authors.affiliation: Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India.
authors.forename: Rinkee
authors.initials: R
authors.lastname: Verma
authors.affiliation: LigDCipher, 729 Nevada Ave, San Mateo, CA 94402, USA.
authors.forename: Erin K
authors.initials: EK
authors.lastname: Bradley
authors.affiliation: Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, 560012, India; Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, 560064, India. Electronic address: vraj@mcbl.iisc.ernet.in.
authors.forename: Valakunja
authors.initials: V
authors.lastname: Nagaraja
mesh_terms: D000995:Antitubercular Agents / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology*; D001499:Bayes Theorem; D017076:Computer-Aided Design; D004264:DNA Topoisomerases, Type I / Q000737:chemistry / Q000378:metabolism*; D004305:Dose-Response Relationship, Drug; D015195:Drug Design; D006801:Humans; D000069550:Machine Learning; D062105:Molecular Docking Simulation; D058990:Molecular Targeted Therapy; D020102:Mycobacterium smegmatis / Q000187:drug effects / Q000201:enzymology / Q000254:growth & development; D009169:Mycobacterium tuberculosis / Q000187:drug effects* / Q000201:enzymology / Q000254:growth & development; D011487:Protein Conformation; D013329:Structure-Activity Relationship; D059004:Topoisomerase I Inhibitors / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology*; D014376:Tuberculosis / Q000188:drug therapy* / Q000382:microbiology
chemical_list: D000995:Antitubercular Agents; D059004:Topoisomerase I Inhibitors; D004264:DNA Topoisomerases, Type I
keywords: Bayesian models; Collaborative drug discovery tuberculosis database; Docking; Function class fingerprints; Homology model; Mycobacterium tuberculosis; Topoisomerase; Tuberculosis