Investigation of NMDA Receptor Channel Blockers in a Series of Methylene Blue Conjugates Using QSAR and Molecular Modeling

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Published: Jun 19, 2019
DOI: https://doi.org/10.18097/BMCRM00091
Keywords:
NMDAR; channel blockers; QSAR; docking

Main Article Content

V.Y. Grigorev
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia
K.A. Shcherbakov
Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
D.E. Polianczyk
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia
A.N. Razdolsky
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia
A.V. Veselovsky
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia; Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
V.V. Grigoriev
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia
A.V. Yarkov
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia
O.A. Raevsky
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severnyi pr., Chernogolovka, 142432 Russia

Abstract

29 conjugates of methylene blue and four chemical structures, including derivatives of carbazole, tetrahydrocarbazole, substituted indoles and γ-carboline, combined with a 1-oxopropylene spacer have been studied as channel blockers of the NMDA receptor (binding site of MK-801) by using four QSAR methods (multiple linear regression, random forest, support vector machine, Gaussian process) and molecular docking. QSAR models have satisfactory characteristics. The analysis of regression models at the statistical level revealed an important role of the hydrogen bond in the complex formation. This was also confirmed by the study of modeled by docking complexes. It was found that the increase in the inhibitory activity of the part of compounds could be attributed to appearance of additional H bonds between the ligands and the receptor.

Article Details

How to Cite
Grigorev, V., Shcherbakov, K., Polianczyk, D., Razdolsky, A., Veselovsky, A., Grigoriev, V., Yarkov, A., & Raevsky, O. (2019). Investigation of NMDA Receptor Channel Blockers in a Series of Methylene Blue Conjugates Using QSAR and Molecular Modeling. Biomedical Chemistry: Research and Methods, 2(2), e00091. https://doi.org/10.18097/BMCRM00091
Issue
Vol. 2 No. 2 (2019)
Section
EXPERIMENTAL RESEARCH

References

  1. Wimo, A., Jonsson, L., Bond, J., Prince, M., Winblad, B. (2013). The worldwide economic impact of dementia 2010. Alzheimer`s Dement., 9(1), 1-11.e3. DOI
  2. Wimo, A., Guerchet, M., Ali, G-C., Wu, Y-T., Prina, A.M., Winblad, B., Jonsson, L., Liu, Z., Prince, M. (2017). The worldwide costs of dementia 2015 and comparisons with 2010. Alzheimer`s Dement., 13(1), 1-7. DOI
  3. Raevsky, O.A., Mukhametov, A., Grigorev, V.Y., Ustyugov, A., Tsay, S-C., Hwu, R.J-R., Yarla, N.S., Barreto, G.E., Aliev, G., Bachurin, S.O. (2018). Applications of Multi-Target Computer-Aided Methodologies in Molecular Design of CNS Drugs. Curr. Med. Chem., 25(39), 5293-5314. DOI
  4. Querfurth, H.W., LaFerla, F.M. (2010). Alzheimer's disease. New Engl. J. Med., 362, 329-344. DOI
  5. Carreiras, M. C., Mendes, E., Perry, M. J., Francisco, A. P., Marco-Contelles, J. (2013). The multifactorial nature of Alzheimer's disease for developing potential therapeutics. Curr. Top Med. Chem., 13, 1745-1770. DOI
  6. Liu H., Wang L., Lv M., Pei R., Li P., Pei Z., Wang Y., Su W., Xie X.-Q. (2014). AlzPlatform: An Alzheimer’s Disease Domain-Specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research. J. Chem. Inf. Model., 54, 1050-1060. DOI
  7. Zambre, V.P., Hambarde, V.A., Petkar, N.N., Patel, C.N., Sawant, S.D. (2015). Structural investigations by in silico modeling for designing NR2B subunit selective NMDA receptor antagonists. RSC Adv., 5, 23922-23940. DOI
  8. Chtita, S., Larif, M., Ghamali, M., Bouachrine, M., Lakhlifi, T. (2015). Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists of N-methyl-d-aspartate based on density functional theory with electronic and topological descriptors. J. Taibah Univ. Sci., 9(2), 143-154. DOI
  9. Elmbarki, C., Elhallaoui, M. (2017). Quantum calculations to construct a 3D-QSAR model based on PCP-TCP derivatives and Molecular Docking with NMDA receptor. J. Mater. Environ. Sci., 8(4), 1391-1400.
  10. Peters, O.M., Connor-Robson, N., Sokolov, V.B., Aksinenko, A.Yu., Kukharsky, M.S., Bachurin, S.O., Ninkina, N., Buchman, V.L. (2013). Chronic Administration of Dimebon Ameliorates Pathology in TauP301S Transgenic Mice. J. Alzheimer’s Dis., 33(4), 1041-1049. DOI
  11. Zhao, M., Liang, F., Xu, H., Yan, W., Zhang, J. (2016). Methylene blue exerts a neuroprotective effect against traumatic brain injury by promoting autophagy and inhibiting microglial activation. Mol. Med. Rep., 13, 13-20. DOI
  12. Raevsky, O.A., Trepalin, S.V., Grigorev, V.Yu., Yarkov, A.V., Bachurin, S.O. (2017) Certificate of state registration of the database "Multitarget organic compounds with potential effects on the central nervous system" № 2017620020.
  13. Coughenour, L.L., Barr, B.M. (2001). Use of Trifluoroperazine Isolates a [3H]Ifenprodil Binding Site in Rat Brain Membranes with the Pharmacology of the Voltage-Independent Ifenprodil Site on N-Methyl-D-aspartate Receptors Containing NR2B Subunits. J. Pharmacol. Exp. Ther. 296, 150-159.
  14. Raevsky, O.A., Grigor'ev, V.Yu., Kireev, D.B., Zefirov, N.S. (1992). Complete Thermodynamic Description of H-Bonding in the Framework of Multiplicative Approach. Quant. Struct.-Act. Relat. 11, 49-63. DOI
  15. Forsythe, G.E., Malcolm, M.A., Moler, C.B.(1977). Computer Methods for Mathematical Computations. Englewood Cliffs, NJ: Prentice-Hall, 270.
  16. URL: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_examples/prog.f
  17. URL: https://github.com/jbcolme/fortran-ls-svm
  18. Obrezanova, O., Csányi, G., Gola, J.M.R., Segall, M.D. (2007). Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties. J. Chem. Inf. Model., 47, 1847-1857. DOI
  19. Mitra, I., Saha, A., Roy, K. (2010). Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol. Simul. 36(13), 1067-1079. DOI
  20. Trott, O., Olson, A.J. (2010). AutoDockVina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 31(2), 455-461. DOI
  21. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 30(16), 2785-2791. DOI
  22. Kiralj, R., Ferreira, M.M.C. (2009). Basic Validation Procedures for Regression Models in QSAR and QSPR Studies: Theory and Application. J. Braz. Chem. Soc. 20(4), 770-787. DOI
  23. Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR. Comb. Sci. 26(5), 694-701. DOI
  24. Bolshakov, K.V., Gmiro, V.E., Tikhonov, D.B., Magazanik, L.G. (2003). Determinants of trapping block of N-methyl- D -aspartate receptor channels. J. Neurochem., 87, 56-65. DOI
  25. Beteringhe, A., Filip, P., Tarko, L. (2005). QSAR study for diarylguanidines, noncompetitive NMDA receptor antagonists. A new topological index AAd derived from local invariants of the chemical graphs of diarylguanidines. Arkivoc., 45-62. DOI
  26. Huettner, J.E., Bean, B.P. (1988). Block of N-methyl-D-aspartate-activated current by the anticonvulsant MK-801: Selective binding to open channels. Proc. Natl. Acad. Sci. USA., 85, 1307-1311. DOI