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Computational explorations to gain insight into the structural features of TNF-α receptor I inhibitors

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Abstract

TNF-α is a crucial cytokine in the process of inflammatory diseases. The adverse effect of TNF-α is mostly mediated by interaction of TNF-α with TNF-α receptor type I (TNFR1); therefore, discovery of molecules which can bind to TNFR1 preventing TNF-α-receptor complex formation would be of great interest. In the current study, using GRID/GOLPE program, a 3D-QSAR study was conducted on a series of synthetic TNFR1 binders, which resulted in a 3D-QSAR model with appropriate power of predictivity in internal (r2 = 0.94 and q2LOO = 0.74) and external (r2 = 0.66 and SDEP = 0.42) validations. The structural features of TNFR1 inhibitors essential for exerting activity were explored by analyzing the contour maps of the 3D-QSAR model showing that steric interactions and hydrogen bonds are responsible for exerting TNFR1 inhibitory activity. To propose potential chemical entities for TNFR1 inhibition, PubChem database was searched and the selected compounds were virtually tested for anti-TNFR1 activity using the generated model, resulting in two potential anti-TNFR1 compounds. Finally, the possible interactions of the compounds with TNFR1 were investigated using docking studies. The findings in the current work can pave the way for designing more potent anti-TNFR1 inhibitors.

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References

  1. E.A. Carswell, L.J. Old, R.L. Kassel, S. Green, N. Fiore, B. Williamson, An endotoxin induced serum factor that cuases necrosis of tumors. Proc. Natl. Acad. Sci. USA. 72(9), 3666–3670 (1975)

    Article  CAS  PubMed  Google Scholar 

  2. I.A. Clark. How TNF was recognized as a key mechanism of disease. Cytokine Growth Factor Rev. 18(3–4), 335–343 (2007)

    Article  CAS  PubMed  Google Scholar 

  3. A. Ceramil, B. Beutler, The role of cachectin/TNF in endotoxic shock and cachexia. Immunol. Today. 9(1), 28–31 (1988)

    Article  Google Scholar 

  4. P. Vandenabeele, W. Declercq, R. Beyaert, W. Fiers, Two tumour necrosis factor receptors: structure and function. Trends Cell. Biol. 5(10), 392–399 (1995)

    Article  CAS  PubMed  Google Scholar 

  5. K.J. Tracey, A. Cerami, Metabolic responses to cachectin/TNF. A brief review. Ann. N. Y. Acad. Sci. 587, 325–331 (1990)

    Article  CAS  PubMed  Google Scholar 

  6. M.J. Elliott, R.N. Maini, M. Feldmann, J.R. Kalden, C. Antoni, J.S. Smolen et al., Randomised double-blind comparison of chimeric monoclonal antibody to tumour necrosis factor α (cA2) versus placebo in rheumatoid arthritis. Lancet. 344(8930), 1105–1110 (1994)

    Article  CAS  PubMed  Google Scholar 

  7. M.A. Palladino, F.R. Bahjat, E.A. Theodorakis, L.L. Moldawer, Anti-TNF-α therapies: the next generation. Nat. Rev. Drug Discov. 2(9), 736–746 (2003)

    Article  CAS  PubMed  Google Scholar 

  8. W.J. Sandborn, S.B. Hanauer, S. Katz, M. Safdi, D.G. Wolf, R.D. Baerg et al., Etanercept for active Crohn’s disease: a randomized, double-blind, placebo-controlled trial. Gastroenterology. 121(5), 1088–1094 (2001)

    Article  CAS  PubMed  Google Scholar 

  9. D. Tracey, L. Klareskog, E.H. Sasso, J.G. Salfeld, P.P. Tak, Tumor necrosis factor antagonist mechanisms of action: a comprehensive review. Pharmacol. Therapeutics. 117(2), 244–279 (2008)

    Article  CAS  Google Scholar 

  10. J.J. Gómez-Reino, L. Carmona, V. Rodríguez Valverde, E.M. Mola, M.D. Montero, Treatment of rheumatoid arthritis with tumor necrosis factor inhibitors may predispose to significant increase in tuberculosis risk: a multicenter active-surveillance report. Arthritis Rheum. 48(8), 2122–2127 (2003)

    Article  CAS  PubMed  Google Scholar 

  11. J.S. Lubel, A.G. Testro, P.W. Angus, Hepatitis B virus reactivation following immunosuppressive therapy: Guidelines for prevention and management. Int. Med. J. 37(10), 705–712 (2007)

    Article  CAS  Google Scholar 

  12. C. Antoni, J. Braun, Side effects of anti-TNF therapy: current knowledge. Clin. Exp. Rheumatol. 20(6 SUPPL. 28), S-152-S-7 (2002)

    Google Scholar 

  13. S.A. Devos, N. Van Den Bossche, M. De Vos, J.M. Naeyaert, Adverse skin reactions to anti-TNF-alpha monoclonal antibody therapy. Dermatology. 206(4), 388–390 (2003)

    Article  CAS  PubMed  Google Scholar 

  14. R. Wolf, H. Matz, E. Orion, V. Ruocco, Anti-TNF therapies—the hope of tomorrow. Clin. Dermatol. 20(5), 522–530 (2002)

    Article  PubMed  Google Scholar 

  15. D. Faustman, M. Davis, TNF receptor 2 pathway: drug target for autoimmune diseases. Nat. Rev. Drug Discov. 9(6), 482–493 (2010)

    Article  CAS  PubMed  Google Scholar 

  16. R.M. Locksley, N. Killeen, M.J. Lenardo, The TNF and TNF receptor superfamilies: Integrating mammalian biology. Cell. 104(4), 487–501 (2001)

    Article  CAS  PubMed  Google Scholar 

  17. M. Leist, F. Gantner, S. Jilg, A. Wendel, Activation of the 55 kDa TNF receptor is necessary and sufficient for TNF- induced liver failure, hepatocyte apoptosis, and nitrite release. J. Immunol. 154(3), 1307–1316 (1995)

    CAS  PubMed  Google Scholar 

  18. L. Mori, S. Iselin, G. De Libero, W. Lesslauer, Attenuation of collagen-induced arthritis in 55-kDa TNF receptor type 1 (TNFR1)-IgG1-treated and TNFR1-deficient mice. J. Immunol. 157(7), 3178–3182 (1996)

    CAS  PubMed  Google Scholar 

  19. M.L. Olleros, R. Guler, N. Corazza, D. Vesin, H.P. Eugster, G. Marchal et al., Transmembrane TNF induces an efficient cell-mediated immunity and resistance to Mycobacterium bovis bacillus Calmette-Guérin infection in the absence of secreted TNF and lymphotoxin-α. J. Immunol. 168(7), 3394–3401 (2002)

    Article  CAS  PubMed  Google Scholar 

  20. B.M. Saunders, S. Tran, S. Ruuls, J.D. Sedgwick, H. Britton, B.J. Warwick, Transmembrane TNF is sufficient to initiate cell migration and granuloma formation and provide acute, but not long-term, control of Mycobacterium tuberculosis infection. J. Immunol. 174(8), 4852–4859 (2005)

    Article  CAS  PubMed  Google Scholar 

  21. Y. Mukai, T. Nakamura, M. Yoshikawa, Y. Yoshioka, S.I. Tsunoda, S. Nakagawa et al., Solution of the structure of the TNF-TNFR2 complex. Sci. Signal. 3(148), ra83 (2010)

    Article  CAS  PubMed  Google Scholar 

  22. T. Banno, A. Gazel, M. Blumenberg, Effects of tumor necrosis factor-α (TNFα) in epidermal keratinocytes revealed using global transcriptional profiling. J. Biol. Chem. 279(31), 32633–32642 (2004)

    Article  CAS  PubMed  Google Scholar 

  23. M.F. Rodrigues, C. Alves, B. Figueiredo, A.B. Rezende, S. Wohlres-Viana, Silva VLd et al., Tumour necrosis factor receptors and apoptosis of alveolar macrophages during early infection with attenuated and virulent Mycobacterium bovis. Immunology. 139(4), 503–512 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. K.A. Zettlitz, V. Lorenz, K. Landauer, S. Münkel, A. Herrmann, P. Scheurich et al., ATROSAB, a humanized antagonistic anti-tumor necrosis factor receptor one-specific antibody. mAbs. 2(6), 639–647 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  25. F.E. McCann, D.P. Perocheau, G. Ruspi, K. Blazek, M.L. Davies, M. Feldmann et al., Selective tumor necrosis factor receptor i blockade is antiinflammatory and reveals immunoregulatory role of tumor necrosis factor receptor II in collagen-induced arthritis. Arthritis Rheumatol. 66(10), 2728–2738 (2014)

    Article  CAS  PubMed  Google Scholar 

  26. P.H. Carter, P.A. Scherle, J.A. Muckelbauer, M.E. Voss, R.Q. Liu, L.A. Thompson et al., Photochemically enhanced binding of small molecules to the tumor necrosis factor receptor-1 inhibits the binding of TNF-α. Proc. Natl. Acad. Sci. USA. 98(21), 11879–11884 (2001)

    Article  CAS  PubMed  Google Scholar 

  27. M.E. Voss, P.H. Carter, A.J. Tebben, P.A. Scherle, G.D. Brown, L.A. Thompson et al., Both 5-arylidene-2-thioxodihydropyrimidine-4,6(1H,5H)-diones and 3-thioxo-2,3-dihydro-1H-imidazo[1,5-a]indol-1-ones are light-dependent tumor necrosis factor-α antagonists. Bioorg. Med. Chem. Lett. 13(3), 533–538 (2003)

    Article  CAS  PubMed  Google Scholar 

  28. J. Tong, Y. Wu, M. Bai, P. Zhan, 3D-QSAR and molecular docking studies on HIV protease inhibitors. J. Mol. Struct. 1129, 17–22 (2017)

    Article  CAS  Google Scholar 

  29. A. Ghaleb, A. Aouidate, M. Ghamali, A. Sbai, M. Bouachrine, T. Lakhlifi, 3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles. J. Mol. Struct. 1145, 278–284 (2017)

    Article  CAS  Google Scholar 

  30. R.D. Cramer Iii, D.E. Patterson, J.D. Bunce, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110(18), 5959–5967 (1988)

    Article  Google Scholar 

  31. P.J. Goodford, A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28(7), 849–857 (1985)

    Article  CAS  PubMed  Google Scholar 

  32. R.C. Wade, K.J. Clark, P.J. Goodford, Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 1. Ligand probe groups with the ability to form two hydrogen bonds. J. Med. Chem. 36(1), 140–147 (1993)

    Article  CAS  PubMed  Google Scholar 

  33. R.C. Wade, P.J. Goodford, Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 2. Ligand probe groups with the ability to form more than two hydrogen bonds. J. Med. Chem. 36(1), 148–156 (1993)

    Article  CAS  PubMed  Google Scholar 

  34. M. Baroni, G. Costantino, G. Cruciani, D. Riganelli, R. Valigi, S. Clementi, Generating Optimal Linear PLS Estimations (GOLPE): An Advanced Chemometric Tool for Handling 3D-QSAR Problems. Quant. Struct. Activ. Relationsh. 12(1), 9–20 (1993)

    Article  CAS  Google Scholar 

  35. HyperChem(TM) Professional 8.0.8; Hypercube, Inc, 1115 NW 4th Street, Gainesville, Florida 32601, USA, (2010)

  36. C.B. Xue, X.T. Chen, X. He, J. Roderick, R.L. Corbett, B. Ghavimi et al., Synthesis and structure-activity relationship of a novel sulfone series of TNF-α converting enzyme inhibitors. Bioorg. Med. Chem. Lett. 14(17), 4453–4459 (2004)

    Article  CAS  PubMed  Google Scholar 

  37. N.L. Allinger. Conformational, analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc. 99(25), 8127–8134 (1977)

    Article  CAS  Google Scholar 

  38. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7 (2016)

  39. P. Goodford, GRID Molecular Discovery Ltd, Oxford, England (1995)

  40. GOLPE 4.5, Multivariate Infometric Analysis; Srl., Viale dei Castagni 16 (Perugia, Italy, 1999)

    Google Scholar 

  41. M. Pastor, G. Cruciani, S. Clementi, Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships. J. Med. Chem. 40(10), 1455–1464 (1997)

    Article  CAS  PubMed  Google Scholar 

  42. G. Cruciani, K.A. Watson, Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. J. Med. Chem. 37(16), 2589–2601 (1994)

    Article  CAS  PubMed  Google Scholar 

  43. K. Roy, S. Kar, P. Ambure, On a simple approach for determining applicability domain of QSAR models. Chemometr. Intell. Lab. Syst. 145, 22–29 (2015)

    Article  CAS  Google Scholar 

  44. A.A. Alizadeh, M. Hamzeh-Mivehroud, B. Sokouti, S. Dastmalchi, An alignment-independent 3D-QSAR study on series of hydroxamic acid-based tumor necrosis factor-α converting enzyme inhibitors. J. Chemometr. 30(9), 537–547 (2016)

    Article  CAS  Google Scholar 

  45. G. Jones, P. Willett, R.C. Glen, Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245(1), 43–53 (1995)

    Article  CAS  PubMed  Google Scholar 

  46. G. Jones, P. Willett, R.C. Glen, A.R. Leach, R. Taylor, Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267(3), 727–748 (1997)

    Article  CAS  PubMed  Google Scholar 

  47. R.A. Laskowski, M.B. Swindells, LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inform. Model. 51(10), 2778–2786 (2011)

    Article  CAS  Google Scholar 

  48. G.L. Wilson, M.A. Lill, Integrating structure-based and ligand-based approaches for computational drug design. Future Med. Chem. 3(6), 735–750 (2011)

    Article  CAS  PubMed  Google Scholar 

  49. C.H. Lee, H.C. Huang, H.F. Juan, Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor. Int. J. Mol. Sci. 12(8), 5304–5318 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. O. Nicolotti, D. Gadaleta, G.F. Mangiatordi, M. Catto, A. Carotti, Applicability domain for QSAR models: where theory meets reality. Int. J. Quant. Struct. Prop. Relationsh. (IJQSPR). 1(1), 45–63 (2016) (2379–7487)

    Google Scholar 

  51. A. Golbraikh, A. Tropsha, Beware of q2! J. Mol. Graph. Model. 20(4), 269–276 (2002)

    Article  CAS  PubMed  Google Scholar 

  52. K. Roy, R.N. Das, P. Ambure, R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr. Intell. Lab. Syst. 152, 18–33 (2016)

    Article  CAS  Google Scholar 

  53. C. Tintori, M. Magnani, S. Schenone, M. Botta, Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors. Eur. J. Med. Chem. 44(3), 990–1000 (2009)

    Article  CAS  PubMed  Google Scholar 

  54. M. Gao, J. Skolnick, A comprehensive survey of small-molecule binding pockets in proteins. PLoS Comput. Biol. 9(10), e1003302 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the Research Office and Biotechnology Research Center of Tabriz University of Medical Sciences for providing financial and facility support.

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Author notes

  1. Mehdi Sharifi and Ali Akbar Alizadeh contributed equally to this work.

Authors and Affiliations

  1. Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Mehdi Sharifi, Ali Akbar Alizadeh, Maryam Hamzeh-Mivehroud & Siavoush Dastmalchi

  2. School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran

    Mehdi Sharifi, Maryam Hamzeh-Mivehroud & Siavoush Dastmalchi

  3. Students Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

    Mehdi Sharifi

  4. Faculty of Pharmacy, Near East University, Mersin 10, PO BOX: 99138, Nicosia, North Cyprus, Turkey

    Siavoush Dastmalchi

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  1. Mehdi Sharifi
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Sharifi, M., Alizadeh, A.A., Hamzeh-Mivehroud, M. et al. Computational explorations to gain insight into the structural features of TNF-α receptor I inhibitors. J IRAN CHEM SOC 15, 2519–2531 (2018). https://doi.org/10.1007/s13738-018-1440-x

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