Skip to content
2000
image of Discovery of Putative GyrB Inhibitors against Mycobacterium tuberculosis: A Combined Virtual Screening and Experimental Study

Abstract

Introduction

With the rapid emergence of drug-resistant strains of tuberculosis, resistance to current first-line and second-line anti-tuberculosis drugs is becoming increasingly prevalent. Consequently, the discovery of new lead compounds is essential to address this challenge. GyrB has emerged as a promising target for tuberculosis treatment due to its pivotal role in DNA replication and topology regulation in

Methods

In this study, a multi-conformational virtual screening approach, complemented by antibacterial activity assays, was utilized to identify novel GyrB inhibitors from the ChemDiv database.

Results

Among the 27 compounds purchased, 10 exhibited significant inhibitory effects against the H37Rv strain, with 8 featuring novel core scaffolds. Notably, three compounds (V027-7669, V017-8710, and 5132-0213) demonstrated a minimum inhibitory concentration (MIC) of 8 μg/mL. Compounds V027-7669 and V017-8710, in particular, showed antibacterial activity against a multidrug-resistant tuberculosis strain, with MIC values of 32 μg/mL and 16 μg/mL, respectively. Molecular dynamics simulations revealed that both V027-7669 and V017-8710 bind stably to GyrB, which are primarily driven by nonpolar interactions. Furthermore, both of them occupy a novel sub-pocket formed by residues Val99, Gly106, Val123, Gly124, and Val125, where they establish hydrogen bonds with Val125.

Conclusion

Our study underscores the effectiveness of a multi-conformational virtual screening strategy in identifying novel GyrB inhibitors and suggests V027-7669 and V017-8710 as promising lead compounds for the development of treatments against multidrug-resistant tuberculosis.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673374736250527040516
2025-06-25
2025-09-02

  • From This Site

    /content/journals/cmc/10.2174/0109298673374736250527040516
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Global tuberculosis report 2024. 2024 Avaialable from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024
  2. Riccardi G. Pasca M.R. Buroni S. Mycobacterium tuberculosis: Drug resistance and future perspectives. Future Microbiol. 2009 4 5 597 614 10.2217/fmb.09.20 19492969
    [Google Scholar]
  3. Telenti A. Imboden P. Marchesi F. Matter L. Schopfer K. Bodmer T. Lowrie D. Colston M.J. Cole S. Detection of rifampicin-resistance mutations in Mycobacterium tuberculosis. Lancet 1993 341 8846 647 651 10.1016/0140‑6736(93)90417‑F 8095569
    [Google Scholar]
  4. Telenti A. Philipp W.J. Sreevatsan S. Bernasconi C. Stockbauer K.E. Wieles B. Musser J.M. Jacobs W.R. Jr. The emb operon, a gene cluster of Mycobacterium tuberculosis involved in resistance to ethambutol. Nat. Med. 1997 3 5 567 570 10.1038/nm0597‑567 9142129
    [Google Scholar]
  5. Kashyap A. Singh P.K. Silakari O. Chemical classes targeting energy supplying GyrB domain of Mycobacterium tuberculosis. Tuberculosis 2018 113 43 54 10.1016/j.tube.2018.09.001 30514513
    [Google Scholar]
  6. Wang J.C. Cellular roles of DNA topoisomerases: A molecular perspective. Nat. Rev. Mol. Cell Biol. 2002 3 6 430 440 10.1038/nrm831 12042765
    [Google Scholar]
  7. Khisimuzi Mdluli Zhenkun M. Mycobacterium tuberculosis DNA gyrase as a target for drug discovery. Infect. Disord. Drug Targets 2007 7 2 159 168 10.2174/187152607781001763 17970226
    [Google Scholar]
  8. Bates A.D. Maxwell A. Energy coupling in type II topoisomerases: Why do they hydrolyze ATP? Biochemistry 2007 46 27 7929 7941 10.1021/bi700789g 17580973
    [Google Scholar]
  9. Barančoková M. Kikelj D. Ilaš J. Recent progress in the discovery and development of DNA gyrase B inhibitors. Future Med. Chem. 2018 10 10 1207 1227 10.4155/fmc‑2017‑0257 29787300
    [Google Scholar]
  10. Bush Natassja G. Evans-Roberts K. Maxwell A. Topoisomerases DNA Topoisomerases. EcoSal Plus 6 2015 6 2 10.1128/ecosalplus.esp‑0010‑2014
    [Google Scholar]
  11. Asif M. Siddiqui A.A. Husain A. Quinolone derivatives as antitubercular drugs. Med. Chem. Res. 2013 22 3 1029 1042 10.1007/s00044‑012‑0101‑3
    [Google Scholar]
  12. Facchinetti V. Gomes C.R. de Souza M.V. Vasconcelos T.R. Perspectives on the development of novel potentially active quinolones against tuberculosis and cancer. Mini Rev. Med. Chem. 2012 12 9 866 874 10.2174/138955712800959099 22512569
    [Google Scholar]
  13. Kathrotiya H.G. Patel M.P. Synthesis and identification of β-aryloxyquinoline based diversely fluorine substituted N-aryl quinolone derivatives as a new class of antimicrobial, antituberculosis and antioxidant agents. Eur. J. Med. Chem. 2013 63 675 684 10.1016/j.ejmech.2013.03.017 23567957
    [Google Scholar]
  14. Liu K.L. Teng F. Xiong L. Li X. Gao C. Yu L.T. Discovery of quinolone derivatives as antimycobacterial agents. RSC Advances 2021 11 39 24095 24115 10.1039/D0RA09250A 35479020
    [Google Scholar]
  15. Dheda K. Mirzayev F. Cirillo D.M. Udwadia Z. Dooley K.E. Chang K.C. Omar S.V. Reuter A. Perumal T. Horsburgh C.R. Jr Murray M. Lange C. Multidrug-resistant tuberculosis. Nat. Rev. Dis. Primers 2024 10 1 22 10.1038/s41572‑024‑00504‑2 38523140
    [Google Scholar]
  16. Maruri F. Sterling T.R. Kaiga A.W. Blackman A. van der Heijden Y.F. Mayer C. Cambau E. Aubry A. A systematic review of gyrase mutations associated with fluoroquinolone-resistant Mycobacterium tuberculosis and a proposed gyrase numbering system. J. Antimicrob. Chemother. 2012 67 4 819 831 10.1093/jac/dkr566 22279180
    [Google Scholar]
  17. Locher C.P. Jones S.M. Hanzelka B.L. Perola E. Shoen C.M. Cynamon M.H. Ngwane A.H. Wiid I.J. van Helden P.D. Betoudji F. Nuermberger E.L. Thomson J.A. A novel inhibitor of gyrase B is a potent drug candidate for treatment of tuberculosis and nontuberculosis mycobacterial infections. Antimicrob. Agents Chemother. 2015 59 3 1455 1465 10.1128/AAC.04347‑14 25534737
    [Google Scholar]
  18. Mani N. Gross C.H. Parsons J.D. Hanzelka B. Müh U. Mullin S. Liao Y. Grillot A.L. Stamos D. Charifson P.S. Grossman T.H. In vitro characterization of the antibacterial spectrum of novel bacterial type II topoisomerase inhibitors of the aminobenzimidazole class. Antimicrob. Agents Chemother. 2006 50 4 1228 1237 10.1128/AAC.50.4.1228‑1237.2006 16569833
    [Google Scholar]
  19. McGarry D.H. Cooper I.R. Walker R. Warrilow C.E. Pichowicz M. Ratcliffe A.J. Salisbury A.M. Savage V.J. Moyo E. Maclean J. Smith A. Charrier C. Stokes N.R. Lindsay D.M. Kerr W.J. Design, synthesis and antibacterial properties of pyrimido[4,5-b]indol-8-amine inhibitors of DNA gyrase. Bioorg. Med. Chem. Lett. 2018 28 17 2998 3003 10.1016/j.bmcl.2018.05.049 30122228
    [Google Scholar]
  20. Chopra S. Matsuyama K. Tran T. Malerich J.P. Wan B. Franzblau S.G. Lun S. Guo H. Maiga M.C. Bishai W.R. Madrid P.B. Evaluation of gyrase B as a drug target in Mycobacterium tuberculosis. J. Antimicrob. Chemother. 2012 67 2 415 421 10.1093/jac/dkr449 22052686
    [Google Scholar]
  21. P S.H. Solapure S. Mukherjee K. Nandi V. Waterson D. Shandil R. Balganesh M. Sambandamurthy V.K. Raichurkar A.K. Deshpande A. Ghosh A. Awasthy D. Shanbhag G. Sheikh G. McMiken H. Puttur J. Reddy J. Werngren J. Read J. Kumar M. R M. Chinnapattu M. Madhavapeddi P. Manjrekar P. Basu R. Gaonkar S. Sharma S. Hoffner S. Humnabadkar V. Subbulakshmi V. Panduga V. Optimization of pyrrolamides as mycobacterial GyrB ATPase inhibitors: structure-activity relationship and in vivo efficacy in a mouse model of tuberculosis. Antimicrob. Agents Chemother. 2014 58 1 61 70 10.1128/AAC.01751‑13 24126580
    [Google Scholar]
  22. Kale M.G. Raichurkar A. P S.H. Waterson D. McKinney D. Manjunatha M.R. Kranthi U. Koushik K. Jena L. Shinde V. Rudrapatna S. Barde S. Humnabadkar V. Madhavapeddi P. Basavarajappa H. Ghosh A. Ramya V.K. Guptha S. Sharma S. Vachaspati P. Kumar K.N.M. Giridhar J. Reddy J. Panduga V. Ganguly S. Ahuja V. Gaonkar S. Kumar C.N.N. Ogg D. Tucker J.A. Boriack-Sjodin P.A. de Sousa S.M. Sambandamurthy V.K. Ghorpade S.R. Thiazolopyridine ureas as novel antitubercular agents acting through inhibition of DNA Gyrase B. J. Med. Chem. 2013 56 21 8834 8848 10.1021/jm401268f 24088190
    [Google Scholar]
  23. Jeankumar V.U. Renuka J. Pulla V.K. Soni V. Sridevi J.P. Suryadevara P. Shravan M. Medishetti R. Kulkarni P. Yogeeswari P. Sriram D. Development of novel N-linked aminopiperidine-based mycobacterial DNA gyrase B inhibitors: Scaffold hopping from known antibacterial leads. Int. J. Antimicrob. Agents 2014 43 3 269 278 10.1016/j.ijantimicag.2013.12.006 24434114
    [Google Scholar]
  24. Reddy K.I. Srihari K. Renuka J. Sree K.S. Chuppala A. Jeankumar V.U. Sridevi J.P. Babu K.S. Yogeeswari P. Sriram D. An efficient synthesis and biological screening of benzofuran and benzo[ d ]isothiazole derivatives for Mycobacterium tuberculosis DNA GyrB inhibition. Bioorg. Med. Chem. 2014 22 23 6552 6563 10.1016/j.bmc.2014.10.016 25456076
    [Google Scholar]
  25. Renuka J. Reddy K.I. Srihari K. Jeankumar V.U. Shravan M. Sridevi J.P. Yogeeswari P. Babu K.S. Sriram D. Design, synthesis, biological evaluation of substituted benzofurans as DNA gyraseB inhibitors of Mycobacterium tuberculosis. Bioorg. Med. Chem. 2014 22 17 4924 4934 10.1016/j.bmc.2014.06.041 25129171
    [Google Scholar]
  26. Jeankumar V.U. Saxena S. Vats R. Reshma R.S. Janupally R. Kulkarni P. Yogeeswari P. Sriram D. Structure-guided discovery of antitubercular agents that target the gyrase ATPase domain. ChemMedChem 2016 11 5 539 548 10.1002/cmdc.201500556 26805396
    [Google Scholar]
  27. Saxena S. Samala G. Renuka J. Sridevi J.P. Yogeeswari P. Sriram D. Development of 2-amino-5-phenylthiophene-3-carboxamide derivatives as novel inhibitors of Mycobacterium tuberculosis DNA GyrB domain. Bioorg. Med. Chem. 2015 23 7 1402 1412 10.1016/j.bmc.2015.02.032 25766629
    [Google Scholar]
  28. Saxena S. Renuka J. Yogeeswari P. Sriram D. Discovery of novel mycobacterial DNA gyrase B inhibitors: In silico and in vitro biological evaluation. Mol. Inform. 2014 33 9 597 609 10.1002/minf.201400058 27486079
    [Google Scholar]
  29. Medapi B. Renuka J. Saxena S. Sridevi J.P. Medishetti R. Kulkarni P. Yogeeswari P. Sriram D. Design and synthesis of novel quinoline–aminopiperidine hybrid analogues as Mycobacterium tuberculosis DNA gyraseB inhibitors. Bioorg. Med. Chem. 2015 23 9 2062 2078 10.1016/j.bmc.2015.03.004 25801151
    [Google Scholar]
  30. Medapi B. Suryadevara P. Renuka J. Sridevi J.P. Yogeeswari P. Sriram D. 4-Aminoquinoline derivatives as novel Mycobacterium tuberculosis GyrB inhibitors: Structural optimization, synthesis and biological evaluation. Eur. J. Med. Chem. 2015 103 1 16 10.1016/j.ejmech.2015.06.032 26318054
    [Google Scholar]
  31. Jeankumar V.U. Reshma R.S. Vats R. Janupally R. Saxena S. Yogeeswari P. Sriram D. Engineering another class of anti-tubercular lead: Hit to lead optimization of an intriguing class of gyrase ATPase inhibitors. Eur. J. Med. Chem. 2016 122 216 231 10.1016/j.ejmech.2016.06.042 27371925
    [Google Scholar]
  32. Jeankumar V.U. Kotagiri S. Janupally R. Suryadevara P. Sridevi J.P. Medishetti R. Kulkarni P. Yogeeswari P. Sriram D. Exploring the gyrase ATPase domain for tailoring newer anti-tubercular drugs: Hit to lead optimization of a novel class of thiazole inhibitors. Bioorg. Med. Chem. 2015 23 3 588 601 10.1016/j.bmc.2014.12.001 25541204
    [Google Scholar]
  33. Omar M.A. Masaret G.S. Abbas E.M.H. Abdel-Aziz M.M. Harras M.F. Farghaly T.A. Novel anti-tubercular and antibacterial based benzosuberone-thiazole moieties: Synthesis, molecular docking analysis, DNA gyrase supercoiling and ATPase activity. Bioorg. Chem. 2020 104 104316 10.1016/j.bioorg.2020.104316 33022549
    [Google Scholar]
  34. Jeankumar V.U. Renuka J. Kotagiri S. Saxena S. Kakan S.S. Sridevi J.P. Yellanki S. Kulkarni P. Yogeeswari P. Sriram D. Gyrase ATPase domain as an antitubercular drug discovery platform: Structure-based design and lead optimization of nitrothiazolyl carboxamide analogues. ChemMedChem 2014 9 8 1850 1859 10.1002/cmdc.201402035 24962352
    [Google Scholar]
  35. Kamsri B. Pakamwong B. Thongdee P. Phusi N. Kamsri P. Punkvang A. Ketrat S. Saparpakorn P. Hannongbua S. Sangswan J. Suttisintong K. Sureram S. Kittakoop P. Hongmanee P. Santanirand P. Leanpolchareanchai J. Goudar K.E. Spencer J. Mulholland A.J. Pungpo P. Bioisosteric design identifies inhibitors of Mycobacterium tuberculosis DNA Gyrase ATPase activity. J. Chem. Inf. Model. 2023 63 9 2707 2718 10.1021/acs.jcim.2c01376 37074047
    [Google Scholar]
  36. Pakamwong B. Thongdee P. Kamsri B. Phusi N. Kamsri P. Punkvang A. Ketrat S. Saparpakorn P. Hannongbua S. Ariyachaokun K. Suttisintong K. Sureram S. Kittakoop P. Hongmanee P. Santanirand P. Spencer J. Mulholland A.J. Pungpo P. Identification of potent DNA gyrase inhibitors active against Mycobacterium tuberculosis. J. Chem. Inf. Model. 2022 62 7 1680 1690 10.1021/acs.jcim.1c01390 35347987
    [Google Scholar]
  37. Pakamwong B. Thongdee P. Kamsri B. Phusi N. Taveepanich S. Chayajarus K. Kamsri P. Punkvang A. Hannongbua S. Sangswan J. Suttisintong K. Sureram S. Kittakoop P. Hongmanee P. Santanirand P. Leanpolchareanchai J. Spencer J. Mulholland A.J. Pungpo P. Ligand-based virtual screening for discovery of indole derivatives as potent DNA Gyrase ATPase inhibitors active against Mycobacterium tuberculosis and hit validation by biological assays. J. Chem. Inf. Model. 2024 64 15 5991 6002 10.1021/acs.jcim.4c00511 38993154
    [Google Scholar]
  38. Fairbrother R.W. Williams B.L. Two new antibiotics; antibacterial activity of novobiocin and vancomycin. Lancet 1956 268 6954 1177 1179 10.1016/S0140‑6736(56)90052‑6 13377703
    [Google Scholar]
  39. Stokes S.S. Vemula R. Pucci M.J. Advancement of GyrB inhibitors for treatment of infections caused by Mycobacterium tuberculosis and non-tuberculous mycobacteria. ACS Infect. Dis. 2020 6 6 1323 1331 10.1021/acsinfecdis.0c00025 32183511
    [Google Scholar]
  40. Zhang Q. Han J. Zhu Y. Yu F. Hu X. Tong H.H.Y. Liu H. Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays. J. Comput. Aided Mol. Des. 2023 37 12 695 706 10.1007/s10822‑023‑00530‑4 37642861
    [Google Scholar]
  41. Gl B. Rajput R. Gupta M. Dahiya P. Thakur J.K. Bhatnagar R. Grover A. Structure-based drug repurposing to inhibit the DNA gyrase of Mycobacterium tuberculosis. Biochem. J. 2020 477 21 4167 4190 10.1042/BCJ20200462 33030198
    [Google Scholar]
  42. Li Q. Shah S. Structure-based virtual screening. Methods Mol Biol 2017 1558 111 124 10.1007/978‑1‑4939‑6783‑4_5 28150235
    [Google Scholar]
  43. Selvaraj C. Panwar U. Dinesh D.C. Boura E. Singh P. Dubey V.K. Singh S.K. Microsecond MD simulation and multiple-conformation virtual screening to identify potential anti-COVID-19 inhibitors against SARS-CoV-2 main protease. Front Chem. 2021 8 595273 10.3389/fchem.2020.595273 33585398
    [Google Scholar]
  44. Zhu J. Jiang Y. Jia L. Xu L. Cai Y. Chen Y. Zhu N. Li H. Jin J. A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ. Mol. Divers. 2021 25 3 1271 1282 10.1007/s11030‑021‑10243‑1 34160714
    [Google Scholar]
  45. Lu G. Ou K. Zhang Y. Zhang H. Feng S. Yang Z. Sun G. Liu J. Wei S. Pan S. Chen Z. Structural analysis, multi-conformation virtual screening and molecular simulation to identify potential inhibitors targeting pS273R proteases of African swine fever virus. Molecules 2023 28 2 570 10.3390/molecules28020570 36677630
    [Google Scholar]
  46. Korb O. Olsson T.S.G. Bowden S.J. Hall R.J. Verdonk M.L. Liebeschuetz J.W. Cole J.C. Potential and limitations of ensemble docking. J. Chem. Inf. Model. 2012 52 5 1262 1274 10.1021/ci2005934 22482774
    [Google Scholar]
  47. Release S. Schrödinger Release 2021-4: Maestro, Schrödinger. 2021 Available from: https://www.schrodinger.com/platform/products/maestro/
  48. Jorgensen W.L. Maxwell D.S. Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996 118 45 11225 11236 10.1021/ja9621760
    [Google Scholar]
  49. Shirude P.S. Madhavapeddi P. Tucker J.A. Murugan K. Patil V. Basavarajappa H. Raichurkar A.V. Humnabadkar V. Hussein S. Sharma S. Ramya V.K. Narayan C.B. Balganesh T.S. Sambandamurthy V.K. Aminopyrazinamides: Novel and specific GyrB inhibitors that kill replicating and nonreplicating Mycobacterium tuberculosis. ACS Chem. Biol. 2013 8 3 519 523 10.1021/cb300510w 23268609
    [Google Scholar]
  50. Agrawal A. Roué M. Spitzfaden C. Petrella S. Aubry A. Hann M. Bax B. Mayer C. Mycobacterium tuberculosis DNA gyrase ATPase domain structures suggest a dissociative mechanism that explains how ATP hydrolysis is coupled to domain motion. Biochem. J. 2013 456 2 263 273 10.1042/BJ20130538 24015710
    [Google Scholar]
  51. Berman H.M. Westbrook J. Feng Z. Gilliland G. Bhat T.N. Weissig H. Shindyalov I.N. Bourne P.E. The protein data bank. Nucleic Acids Res. 2000 28 1 235 242 10.1093/nar/28.1.235 10592235
    [Google Scholar]
  52. Chemical Computing Group ULC, 910-1010. 2024 Available from: https://www.chemcomp.com/en/Products.htm
  53. Kim S. Chen J. Cheng T. Gindulyte A. He J. He S. Li Q. Shoemaker B.A. Thiessen P.A. Yu B. Zaslavsky L. Zhang J. Bolton E.E. PubChem 2023 update. Nucleic Acids Res. 2023 51 D1 D1373 D1380 10.1093/nar/gkac956 36305812
    [Google Scholar]
  54. Frisch A. Gaussian 09W Reference, Wallingford, USA. 2009 Avaialable from: https://gaussian.com/g09citation/
  55. Kristyán S. Ruzsinszky A. Csonka G.I. Accurate thermochemistry from corrected Hartree-Fock results: Rapid estimation of nearly experimental quality total energy using the small 6-31G(d) basis set. Theor. Chem. Acc. 2001 106 5 319 328 10.1007/s002140100282
    [Google Scholar]
  56. Fox T. Kollman P.A. Application of the RESP methodology in the parametrization of organic solvents. J. Phys. Chem. B 1998 102 41 8070 8079 10.1021/jp9717655
    [Google Scholar]
  57. Case D.A. Aktulga H.M. Belfon K. Ben-Shalom I. Brozell S.R. Cerutti D.S. Cheatham T.E. III Cruzeiro V.W.D. Darden T.A. Duke R.E. Amber 2021. San Francisco University of California 2021
    [Google Scholar]
  58. Wang J. Wolf R.M. Caldwell J.W. Kollman P.A. Case D.A. Development and testing of a general amber force field. J. Comput. Chem. 2004 25 9 1157 1174 10.1002/jcc.20035 15116359
    [Google Scholar]
  59. Maier J.A. Martinez C. Kasavajhala K. Wickstrom L. Hauser K.E. Simmerling C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 2015 11 8 3696 3713 10.1021/acs.jctc.5b00255 26574453
    [Google Scholar]
  60. Jorgensen W.L. Chandrasekhar J. Madura J.D. Impey R.W. Klein M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983 79 2 926 935 10.1063/1.445869
    [Google Scholar]
  61. Price D.J. Brooks C.L. III. A modified TIP3P water potential for simulation with Ewald summation. J. Chem. Phys. 2004 121 20 10096 10103 10.1063/1.1808117 15549884
    [Google Scholar]
  62. Martyna G.J. Hughes A. Tuckerman M.E. Molecular dynamics algorithms for path integrals at constant pressure. J. Chem. Phys. 1999 110 7 3275 3290 10.1063/1.478193
    [Google Scholar]
  63. Toukmaji A. Sagui C. Board J. Darden T. Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions. J. Chem. Phys. 2000 113 24 10913 10927 10.1063/1.1324708
    [Google Scholar]
  64. Miyamoto S. Kollman P.A. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 1992 13 8 952 962 10.1002/jcc.540130805
    [Google Scholar]
  65. Stein R.M. Yang Y. Balius T.E. O’Meara M.J. Lyu J. Young J. Tang K. Shoichet B.K. Irwin J.J. Property-unmatched decoys in docking benchmarks. J. Chem. Inf. Model. 2021 61 2 699 714 10.1021/acs.jcim.0c00598 33494610
    [Google Scholar]
  66. Mysinger M.M. Carchia M. Irwin J.J. Shoichet B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 2012 55 14 6582 6594 10.1021/jm300687e 22716043
    [Google Scholar]
  67. Spiegel J. Senderowitz H. A comparison between enrichment optimization algorithm (EOA)-based and docking-based virtual screening. Int. J. Mol. Sci. 2021 23 1 43 10.3390/ijms23010043 35008467
    [Google Scholar]
  68. Bender A. Glen R.C. A discussion of measures of enrichment in virtual screening: Comparing the information content of descriptors with increasing levels of sophistication. J. Chem. Inf. Model. 2005 45 5 1369 1375 10.1021/ci0500177 16180913
    [Google Scholar]
  69. Fawcett T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006 27 8 861 874 10.1016/j.patrec.2005.10.010
    [Google Scholar]
  70. Lipinski C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 2000 44 1 235 249 10.1016/S1056‑8719(00)00107‑6 11274893
    [Google Scholar]
  71. Collins L. Franzblau S.G. Microplate alamar blue assay versus BACTEC 460 system for high-throughput screening of compounds against Mycobacterium tuberculosis and Mycobacterium avium. Antimicrob. Agents Chemother. 1997 41 5 1004 1009 10.1128/AAC.41.5.1004 9145860
    [Google Scholar]
  72. Genheden S. Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015 10 5 449 461 10.1517/17460441.2015.1032936 25835573
    [Google Scholar]
  73. Onufriev A. Bashford D. Case D.A. Modification of the generalized Born model suitable for macromolecules. J. Phys. Chem. B 2000 104 15 3712 3720 10.1021/jp994072s
    [Google Scholar]
  74. Wang E. Sun H. Wang J. Wang Z. Liu H. Zhang J.Z.H. Hou T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev. 2019 119 16 9478 9508 10.1021/acs.chemrev.9b00055 31244000
    [Google Scholar]
  75. Forouzesh N. Mishra N. An effective MM/GBSA protocol for absolute binding free energy calculations: A case study on SARS-CoV-2 spike protein and the human ACE2 receptor. Molecules 2021 26 8 2383 10.3390/molecules26082383 33923909
    [Google Scholar]
  76. Weng G. Wang E. Chen F. Sun H. Wang Z. Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein–peptide complexes. Phys. Chem. Chem. Phys. 2019 21 19 10135 10145 10.1039/C9CP01674K 31062799
    [Google Scholar]
  77. Pattar S.V. Adhoni S.A. Kamanavalli C.M. Kumbar S.S. In silico molecular docking studies and MM/GBSA analysis of coumarin-carbonodithioate hybrid derivatives divulge the anticancer potential against breast cancer. Beni. Suef Univ. J. Basic Appl. Sci. 2020 9 1 36 10.1186/s43088‑020‑00059‑7
    [Google Scholar]
  78. Tian M. Li H. Yan X. Gu J. Zheng P. Luo S. Zhangsun D. Chen Q. Ouyang Q. Application of per-residue energy decomposition to design peptide inhibitors of PSD95 GK domain. Front. Mol. Biosci. 2022 9 848353 10.3389/fmolb.2022.848353 35433833
    [Google Scholar]
  79. Gilson M.K. Davis M.E. Luty B.A. McCammon J.A. Computation of electrostatic forces on solvated molecules using the Poisson-Boltzmann equation. J. Phys. Chem. 1993 97 14 3591 3600 10.1021/j100116a025
    [Google Scholar]
  80. Sitkoff D. Sharp K.A. Honig B. Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem. 1994 98 7 1978 1988 10.1021/j100058a043
    [Google Scholar]
  81. Franzblau S.G. Witzig R.S. McLaughlin J.C. Torres P. Madico G. Hernandez A. Degnan M.T. Cook M.B. Quenzer V.K. Ferguson R.M. Gilman R.H. Rapid, low-technology MIC determination with clinical Mycobacterium tuberculosis isolates by using the microplate Alamar Blue assay. J. Clin. Microbiol. 1998 36 2 362 366 10.1128/JCM.36.2.362‑366.1998 9466742
    [Google Scholar]
  82. Fu L. Shi S. Yi J. Wang N. He Y. Wu Z. Peng J. Deng Y. Wang W. Wu C. Lyu A. Zeng X. Zhao W. Hou T. Cao D. ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res. 2024 52 W1 W422 W431 10.1093/nar/gkae236 38572755
    [Google Scholar]
  83. Banerjee P. Kemmler E. Dunkel M. Preissner R. ProTox 3.0: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024 52 W1 W513 W520 10.1093/nar/gkae303 38647086
    [Google Scholar]
  84. Xue W. Zuo X. Zhao X. Wang X. Zhang X. Xia J. Cheng M. Yang H. Bioisosteric replacement strategy leads to novel DNA gyrase B inhibitors with improved potencies and properties. Bioorg. Chem. 2024 147 107314 10.1016/j.bioorg.2024.107314 38581967
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673374736250527040516
Loading
Discovery of Putative GyrB Inhibitors against Mycobacterium tuberculosis: A Combined Virtual Screening and Experimental Study
Bentham Science Publishers ; https://doi.org/10.2174/0109298673374736250527040516
/content/journals/cmc/10.2174/0109298673374736250527040516
/content/journals/cmc/10.2174/0109298673374736250527040516
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: binding free energy ; multi-conformational virtual screening ; Mycobacterium tuberculosis ; GyrB inhibitors ; molecular dynamics simulation ; Tuberculosis

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test