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In silico discovery of potential azole-containing mPGES-1 inhibitors by virtual screening, pharmacophore modeling and molecular dynamics simulations

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Abstract

Inhibition of microsomal prostaglandin E2 synthase-1 (mPGES-1) is promising for designing novel nonsteroidal anti-inflammatory drugs, as they lack side-effects associated with inhibition of cyclooxygenase enzymes. Azole compounds are nitrogen-containing heterocycles and have a wide use in medicine and are considered as promising compounds in medicinal chemistry. Various computer-aided drug design strategies are incorporated in this study. Structure-based virtual screening was performed employing various docking programs. Receiver operator characteristic (ROC) curves were used to evaluate the selectivity of each program. Furthermore, scoring power of Autodock4 and Autodock Vina was assessed by Pearson’s correlation coefficients. Pharmacophore models were generated and Güner-Henry score of the best model was calculated as 0.89. Binding modes of the final 10 azole compounds were analyzed and further investigation of the best binding (− 8.38 kcal/mol) compound was performed using molecular dynamics simulation, revealing that furazan1224 (ZINC001142847306) occupied the binding site of the substrate, prostaglandin H2 (PGH2) and remained stable for 100 ns. Continuous hydrogen bonds and hydrophobic interactions with amino acids in the active site supported the stability of furazan1224 throughout the trajectory. Pharmacokinetic profile showed that furazan1224 lacks the risks of inhibiting cytochrome P450 3A4 enzyme and central nervous system-related side-effects.

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References

  1. Cushing H (1964) The basophil adenomas of the pituitary body and their clinical manifestations (pituitary basophilism). J Neurosurg 21:318–347

    Article  Google Scholar 

  2. Williams TJ, O’Hehir RE, Czarny D, Horne M, Bowes G (1988) Acute myopathy in severe acute asthma treated with intravenously. Am Rev Respir Dis 137:460–463

    Article  CAS  PubMed  Google Scholar 

  3. Hasselgren PO (1999) Glucocorticoids and muscle catabolism. Curr Opin Clin Nutr Metab Care 2:201–205

    Article  CAS  PubMed  Google Scholar 

  4. Mukherjee D, Nissen SE, Topol EJ (2001) Risk of cardiovascular events associated with selective COX-2 inhibitors. JAMA 286:954–959

    Article  CAS  PubMed  Google Scholar 

  5. Praveen Rao PN, Knaus EE (2008) Evolution of nonsteroidal anti-inflammatory drugs (NSAIDs): cyclooxygenase (COX) inhibition and beyond. J Pharm Pharm Sci 11:81–110

    Article  Google Scholar 

  6. Serhan CN, Ward PA, Gilroy DW (2010) Fundamentals of inflammation (Cambridge University Press)

  7. Bülbül B, Küçükgüzel İ (2019) Microsomal prostaglandin E2 synthase-1 as a new macromolecular drug target in the prevention of inflammation and cancer. Anticancer Agents Med Chem 19:1205–1222

    Article  PubMed  CAS  Google Scholar 

  8. Bhagat J, Singh N, Nishimura N, Shimada Y (2021) A comprehensive review on environmental toxicity of azole compounds to fish. Chemosphere 262:128335

  9. Wright JB (1951) The chemistry of the benzimidazoles. Chem Rev 48:397–541

    Article  CAS  PubMed  Google Scholar 

  10. Bhatnagar A, Sharma PK, Kumar N (2011) A review on Imidazoles their chemistry and pharmacological potentials. Int J PharmTech Res 3:268–282

    CAS  Google Scholar 

  11. Gaba M, Mohan C (2016) Development of drugs based on imidazole and benzimidazole bioactive heterocycles: Recent advances and future directions. Springer, US

    Google Scholar 

  12. Sharma PC, Sinhmar A, Sharma A, Rajak H, Pathak DP (2013) Medicinal significance of benzothiazole scaffold: an insight view. J Enzyme Inhib Med Chem 28:240–266

    Article  PubMed  Google Scholar 

  13. Keri RS, Patil MR, Patil SA, Budagumpi S (2015) A comprehensive review in current developments of benzothiazole-based molecules in medicinal chemistry. Eur J Med Chem 89:207–251

    Article  CAS  PubMed  Google Scholar 

  14. Smith B, Chang H-H, Medda F, Gokhale V, Dietrich J, Davis A, Meuillet EJ, Hulme C (2012) Synthesis and biological activity of 2-aminothiazoles as novel inhibitors of PGE2 production in cells. Bioorg Med Chem Lett 22:3567–3570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. He S, Li C, Liu Y, Lai L (2013) Discovery of highly potent microsomal prostaglandin e2 synthase 1 inhibitors using the active conformation structural model and virtual screen. J Med Chem 56:3296–3309

    Article  CAS  PubMed  Google Scholar 

  16. Erensoy G, Ding K, Zhan C-G, Elmezayen AD, Yelekçi K, Duracık M, Özakpınar ÖB, Küçükgüzel İ (2020) Synthesis, in silico studies and cytotoxicity evaluation of novel 1, 3, 4-oxadiazole derivatives designed as potential mPGES-1 inhibitors. J Res Pharm 24:436–451

    CAS  Google Scholar 

  17. Shoichet BK (2006) Virtual screening of chemical libraries. Nature 432:862–865

    Article  CAS  Google Scholar 

  18. Bologa CG, Ursu O, Oprea TI (2019) How to prepare a compound collection prior to virtual screening. In Bioinformatics and Drug Discovery., (New York, NY: Humana Press), pp. 119–138

  19. Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M, Pujadas G, Garcia-Vallvé S (2019) The light and dark sides of virtual screening: What is there to know? Int J Mol Sci 20

  20. Khedkar SA, Malde AK, Coutinho EC, Srivastava S (2007) Pharmacophore modeling in drug discovery and development: an overview. Med Chem (Los Angeles) 3:187–197

    CAS  Google Scholar 

  21. Ozalp L, Sağ Erdem S, Yüce-Dursun B, Mutlu O, Özbil M (2018) Computational insight into the phthalocyanine-DNA binding via docking and molecular dynamics simulations. Comput Biol Chem 77

  22. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: An open chemical toolbox. J Cheminform 3:33

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. BIOVIA (2016) Discovery Studio Modeling Environment

  25. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  26. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P, Barril X, Hubbard RE, Morley SD (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 10:1–7

    Article  CAS  Google Scholar 

  27. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Partridge KM, Antonysamy S, Bhattachar SN, Chandrasekhar S, Fisher MJ, Fretland A, Gooding K, Harvey A, Hughes NE, Kuklish SL, Luz JG, Manninen PR, McGee JE, Mudra DR, Navarro A, Norman BH, Quimby SJ, Schiffler MA, Sloan AV, Warshawsky AM, Weller JM, York JS, Yu XP (2017) Discovery and characterization of [(cyclopentyl)ethyl]benzoic acid inhibitors of microsomal prostaglandin E synthase-1. Bioorganic Med Chem Lett 27:1478–1483

    Article  CAS  Google Scholar 

  30. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Trott O, Olson AJ (2010) Software news and update autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Guner O, Clement O, Kurogi Y (2004) Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Curr Med Chem 11:2991–3005

    Article  PubMed  Google Scholar 

  33. Frisch M, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA (2009) Gaussian 09. Gaussian Inc., Wallingford, CT

  34. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662

    Article  CAS  Google Scholar 

  36. Jorgensen W, Chandrasekhar J, Madura J, Impey R, Klein M (1983) Comparison of Simple Potential Functions for Simulating Liquid Water. J Chem Phys 79:926–935

    Article  CAS  Google Scholar 

  37. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindah E (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25

    Article  Google Scholar 

  38. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:14101

    Article  CAS  Google Scholar 

  39. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472

    Article  CAS  Google Scholar 

  40. Darden TA, York D, Pedersen LJ (1993) Particle mesh Ewald: An Nṡlog(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  41. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190

    Article  CAS  Google Scholar 

  42. Daura X, Gademann K, Jaun B, Seebach D, van Gunsteren WF, Mark AE (1999) Peptide folding: when simulation meets experiment. Angew Chemie Int Ed 38:236–240

    Article  CAS  Google Scholar 

  43. Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the receiver operating characteristic curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547

    Article  CAS  PubMed  Google Scholar 

  44. Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, Tian S, Hou T (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Phys 18:12964–12975

    Article  CAS  Google Scholar 

  45. Jang C, Yadav DK, Subedi L, Venkatesan R, Venkanna A, Afzal S, Lee E, Yoo J, Ji E, Kim SY (2018) Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay. Sci Rep 8:1–21

    Google Scholar 

  46. Zeb A, Son M, Yoon S, Kim JH, Park SJ, Lee KW (2019) Computational simulations identified two candidate inhibitors of Cdk5/p25 to abrogate tau-associated neurological disorders. Comput Struct Biotechnol J 17:579–590

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sjögren T, Nord J, Ek M, Johansson P, Liu G, Geschwindner S (2013) Crystal structure of microsomal prostaglandin E2 synthase provides insight into diversity in the MAPEG superfamily. Proc Natl Acad Sci U S A 110:3806–3811

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jegerschold C, Pawelzik SC, Purhonen P, Bhakat P, Gheorghe K, Gyobu N, Mitsuoka K, Morgenstern R, Jakobsson PJ, Hebert H (2008) Structural basis for induced formation of the inflammatory mediator prostaglandin E2. Proc Natl Acad Sci U S A 105:11110–11115

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lin JH, Yamazaki M (2003) Role of P-glycoprotein in pharmacokinetics. Clin Pharmacokinet 42:59–98

    Article  CAS  PubMed  Google Scholar 

  50. Van De Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204

    Article  PubMed  Google Scholar 

  51. Manikandan P, Nagini S (2018) Cytochrome P450 structure, function and clinical significance: a review. Curr Drug Targets 19:38–54

    Article  CAS  PubMed  Google Scholar 

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Funding

Author L. Ozalp has received research support from the Council of Higher Education (YÖK) of Turkey (PhD scholarship of 100/2000 program) and TUBİTAK 2211/C National PhD Scholarship Program in the Priority Fields in Science and Technology.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Arts and Sciences, Marmara University, Istanbul, Turkey

    Lalehan Ozalp & Ayşe Ogan

  2. Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Marmara University, Istanbul, Turkey

    İlkay Küçükgüzel

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  1. Lalehan Ozalp
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  2. İlkay Küçükgüzel
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Lalehan Ozalp: research, analysis, writing. İlkay Küçükgüzel: writing, revision, editing. Ayşe Ogan: revision, editing.

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Correspondence to Lalehan Ozalp.

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Ozalp, L., Küçükgüzel, İ. & Ogan, A. In silico discovery of potential azole-containing mPGES-1 inhibitors by virtual screening, pharmacophore modeling and molecular dynamics simulations. Struct Chem 33, 1157–1175 (2022). https://doi.org/10.1007/s11224-022-01911-5

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