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image of In Silico ADMET Studies, Molecular Docking and Molecular Dynamics Simulation of Thiadiazole Derivatives for the Identification of Putative HsaA Monooxygenase Inhibitors

Abstract

Introduction

The rise of drug-resistant strains of (Mtb) represents a substantial public health challenge. Current TB treatments involve the combination of several antibiotics and other agents. However, the development of drug resistance, reduced bioavailability, and elevated toxicity have rendered most of the drugs less effective.

Methods

To resolve this problem, the identification of novel anti-tuberculosis agents with novel mechanisms of action is the need of the hour. HsaA monooxygenase is an enzyme involved in cholesterol metabolism, particularly in certain strains of Mycobacterium bacteria. This research focuses on discovering new inhibitors for HsaA from a pool of 40 compounds using computational techniques like molecular docking and Molecular Dynamics (MD) simulations along with comparing it with GSK2556286.

Results

Docking studies revealed that AK05 and AK13 showed good binding affinity as compared to . The docking scores of AK05, AK13, and are -9.4, -9.0, and -8.9 kcal/mol, respectively. ADMET studies showed that these thiadiazole derivatives can be investigated as lead molecules for the development of novel antituberculosis drugs. MD simulation studies showed that both of the compounds AK05 and AK13 were stable at the binding site with RMSD below 0.25 nm.

Conclusion

All these findings demonstrated that AK05 and AK13 could be used as potent compounds for the development of HsaA monooxygenase inhibitors.

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2025-04-03
2025-09-08

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References

  1. Mabhula A. Singh V. Drug-resistance in Mycobacterium tuberculosis : Where we stand. MedChemComm 2019 10 8 1342 1360 10.1039/C9MD00057G 31534654
    [Google Scholar]
  2. Kobayashi K. Ato M. Matsumoto S. Global threats and the control of multidrug-resistant tuberculosis. J. Disaster Res. 2011 6 4 443 450 10.20965/jdr.2011.p0443
    [Google Scholar]
  3. Chakaya J. Petersen E. Nantanda R. Mungai B.N. Migliori G.B. Amanullah F. Lungu P. Ntoumi F. Kumarasamy N. Maeurer M. Zumla A. The WHO Global Tuberculosis The WHO global tuberculosis 2021 report – not so good news and turning the tide back to End TB. Int. J. Infect. Dis. 2022 124 Suppl. 1 S26 S29 10.1016/j.ijid.2022.03.011 35321845
    [Google Scholar]
  4. Subhasree C. Priya R.S.K. Diwakar M. Subramaniam S. Shyama S. Review on comparative genomics for mycobacterium tuberculosis strains. Int. J. Pharm. Sci. Res. 2017 8 12 5022 5042
    [Google Scholar]
  5. Gomez J.E. McKinney J.D. M. tuberculosis persistence, latency, and drug tolerance. Tuberculosis 2004 84 1-2 29 44 10.1016/j.tube.2003.08.003 14670344
    [Google Scholar]
  6. Queval C.J. Brosch R. Simeone R. The macrophage: A disputed fortress in the battle against Mycobacterium tuberculosis. Front. Microbiol. 2017 8 2284 10.3389/fmicb.2017.02284 29218036
    [Google Scholar]
  7. Ouellet H. Johnston J.B. Montellano P.R.O. Cholesterol catabolism as a therapeutic target in Mycobacterium tuberculosis. Trends Microbiol. 2011 19 11 530 539 10.1016/j.tim.2011.07.009 21924910
    [Google Scholar]
  8. Wipperman M.F. Sampson N.S. Thomas S.T. Pathogen roid rage: Cholesterol utilization by Mycobacterium tuberculosis. Crit. Rev. Biochem. Mol. Biol. 2014 49 4 269 293 10.3109/10409238.2014.895700 24611808
    [Google Scholar]
  9. Griffin J.E. Gawronski J.D. DeJesus M.A. Ioerger T.R. Akerley B.J. Sassetti C.M. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog. 2011 7 9 e1002251 10.1371/journal.ppat.1002251 21980284
    [Google Scholar]
  10. Mdluli K. Kaneko T. Upton A. The tuberculosis drug discovery and development pipeline and emerging drug targets. Cold Spring Harb. Perspect. Med. 2015 5 6 a021154 10.1101/cshperspect.a021154 25635061
    [Google Scholar]
  11. Dresen C. Lin L.Y.C. D’Angelo I. Tocheva E.I. Strynadka N. Eltis L.D. A flavin-dependent monooxygenase from Mycobacterium tuberculosis involved in cholesterol catabolism. J. Biol. Chem. 2010 285 29 22264 22275 10.1074/jbc.M109.099028 20448045
    [Google Scholar]
  12. Nesbitt N.M. Yang X. Fontán P. Kolesnikova I. Smith I. Sampson N.S. Dubnau E. A thiolase of Mycobacterium tuberculosis is required for virulence and production of androstenedione and androstadienedione from cholesterol. Infect. Immun. 2010 78 1 275 282 10.1128/IAI.00893‑09 19822655
    [Google Scholar]
  13. Singh R. Dwivedi S.P. Gaharwar U.S. Meena R. Rajamani P. Prasad T. Recent updates on drug resistance in Mycobacterium tuberculosis. J. Appl. Microbiol. 2020 128 6 1547 1567 10.1111/jam.14478 31595643
    [Google Scholar]
  14. Black T.A. Buchwald U.K. The pipeline of new molecules and regimens against drug-resistant tuberculosis. J. Clin. Tuberc. Other Mycobact. Dis. 2021 25 100285 10.1016/j.jctube.2021.100285 34816020
    [Google Scholar]
  15. Nuermberger E.L. Martínez-Martínez M.S. Sanz O. Urones B. Esquivias J. Soni H. Tasneen R. Tyagi S. Li S.Y. Converse P.J. Boshoff H.I. Robertson G.T. Besra G.S. Abrahams K.A. Upton A.M. Mdluli K. Boyle G.W. Turner S. Fotouhi N. Cammack N.C. Siles J.M. Alonso M. Escribano J. Lelievre J. Rullas-Trincado J. Pérez-Herrán E. Bates R.H. Maher-Edwards G. Barros D. Ballell L. Jiménez E. GSK2556286 is a novel antitubercular drug candidate effective in vivo with the potential to shorten tuberculosis treatment. Antimicrob. Agents Chemother. 2022 66 6 e00132-22 10.1128/aac.00132‑22 35607978
    [Google Scholar]
  16. Sellamuthu S. Sellapan M. Kandasamy C. Kumar D. Kasimayan K. Manickavasagam S. Analog-based design and development of novel molecules as anti-tubercular agents. Indian J. Pharm. Educ. Res. 2023 57 1 S105 S113
    [Google Scholar]
  17. Kumar A. Kumar B. Bhatia R. Design, synthesis, molecular docking, and biological evaluation of isatin-based fused heterocycles as epidermal growth factor receptor inhibitors. Assay Drug Dev. Technol. 2023 21 5 222 233 10.1089/adt.2022.120 37439798
    [Google Scholar]
  18. Bekker H. Berendsen H. Dijkstra E. Achterop S. Vondrumen R.v. Vanderspoel D. Sijbers A. Keegstra H. Renardus M. Gromacs-a parallel computer for molecular- dynamics simulations. 4th international conference on computational physics (PC 92) World Scientific Publishing, Singapore, 1993, pp 252-256.
    [Google Scholar]
  19. Ganesan A. Coote M.L. Barakat K. Molecular dynamics-driven drug discovery: Leaping forward with confidence. Drug Discov. Today 2017 22 2 249 269 10.1016/j.drudis.2016.11.001 27890821
    [Google Scholar]
  20. Thalla M. Kant K. Dalchand Rawat R. Banerjee S. Merged experimental guided computational strategy toward tuberculosis treatment mediated by alveolar macrophages mannose receptor. J. Biomol. Struct. Dyn. 2020 38 17 5195 5203 10.1080/07391102.2019.1697369 31779532
    [Google Scholar]
  21. Schmid N. Eichenberger A.P. Choutko A. Riniker S. Winger M. Mark A.E. van Gunsteren W.F. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J. 2011 40 7 843 856 10.1007/s00249‑011‑0700‑9 21533652
    [Google Scholar]
  22. van Aalten D.M.F. Bywater R. Findlay J.B.C. Hendlich M. Hooft R.W.W. Vriend G. PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules. J. Comput. Aided Mol. Des. 1996 10 3 255 262 10.1007/BF00355047 8808741
    [Google Scholar]
  23. Mark P. Nilsson L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 2001 105 43 9954 9960 10.1021/jp003020w
    [Google Scholar]
  24. Van Gunsteren W.F. Berendsen H.J.C. A leap-frog algorithm for stochastic dynamics. Mol. Simul. 1988 1 3 173 185 10.1080/08927028808080941
    [Google Scholar]
  25. Berendsen H.J.C. van der Spoel D. van Drunen R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995 91 1-3 43 56 10.1016/0010‑4655(95)00042‑E
    [Google Scholar]
  26. Hess B. Bekker H. Berendsen H.J.C. Fraaije J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 1997 18 12 1463 1472 10.1002/(SICI)1096‑987X(199709)18:12<1463::AID‑JCC4>3.0.CO;2‑H
    [Google Scholar]
  27. Di Pierro M. Elber R. Leimkuhler B. A stochastic algorithm for the isobaric–isothermal ensemble with Ewald summations for all long range forces. J. Chem. Theory Comput. 2015 11 12 5624 5637 10.1021/acs.jctc.5b00648 26616351
    [Google Scholar]
  28. Humphrey W. Dalke A. Schulten K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996 14 1 33 38, 27-28 10.1016/0263‑7855(96)00018‑5 8744570
    [Google Scholar]
  29. Rawat R. Kant K. Kumar A. Bhati K. Verma S.M. HeroMDAnalysis: An automagical tool for GROMACS-based molecular dynamics simulation analysis. Future Med. Chem. 2021 13 5 447 456 10.4155/fmc‑2020‑0191 33496197
    [Google Scholar]
  30. Devi S. Rangra N.K. Rawat R. Alrobaian M.M. Alam A. Singh R. Singh A. Anti-atherogenic effect of Nepitrin-7-O-glucoside: A flavonoid isolated from Nepeta hindostana via acting on PPAR – α receptor. Steroids 2021 165 108770 10.1016/j.steroids.2020.108770 33227319
    [Google Scholar]
  31. Vaught A. Graphing with Gnuplot and Xmgr: Two graphing packages available under linux. Linux J 1996 1996 28es 7
    [Google Scholar]
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In Silico ADMET Studies, Molecular Docking and Molecular Dynamics Simulation of Thiadiazole Derivatives for the Identification of Putative HsaA Monooxygenase Inhibitors
Bentham Science Publishers ; https://doi.org/10.2174/0109298673346116250227101530
/content/journals/cmc/10.2174/0109298673346116250227101530
/content/journals/cmc/10.2174/0109298673346116250227101530
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  • Article Type:     Research Article
Keywords: molecular docking ; binding affinity ; anti-tuberculosis agents ; MD simulation ; HsaA monooxygenase ; GSK2556286

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