Computational Evaluation of Selectivity of Inhibition of Muscarinic Receptors M1-M4

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Published: Sep 7, 2018
DOI: https://doi.org/10.18097/BMCRM00072
Keywords:
acetylcholine muscarinic receptors inhibitors comparative inhibition docking computational methods molecular dynamics QSAR

Main Article Content

A.V. Mikurova
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia; Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
V.S. Skvortsov
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia; Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia
O.A. Raevsky
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia

Abstract

A set of models for preliminary estimation of the inhibition constant values of potential ligands for the 4 acetylcholine muscarinic receptors M1-M4 was developed. The study uses an information about three-dimensional structure of human M1, M2 and M4 receptors, as well as the M3 receptor model, constructed by homology based on the structure of the rat M3 receptor. The Ki values for 42 compounds were obtained from the sources. Modeling of “protein-ligand” complexes was performed using molecular docking and molecular dynamics procedures. The component energy characteristics of the complexes were calculated from data obtained from simulation of molecular dynamics by the MM-PBSA/MM-GBSA methods. These characteristics were used as independent variables to construct the linear regression equations for pKi value predicting. The equations obtained for each receptors allow us to predict pKi with an average accuracy of 0.65 logarithmic units.

Article Details

How to Cite
Mikurova, A., Skvortsov, V., & Raevsky, O. (2018). Computational Evaluation of Selectivity of Inhibition of Muscarinic Receptors M1-M4. Biomedical Chemistry: Research and Methods, 1(3), e00072. https://doi.org/10.18097/BMCRM00072
Issue
Vol. 1 No. 3 (2018)
Section
EXPERIMENTAL RESEARCH

References

  1. Eglen, R. M. (2006). Muscarinic receptor subtypes in neuronal and non?neuronal cholinergic function. Autonomic and Autacoid Pharmacology, 26(3), 219-233. DOI
  2. Langmead, C. J., Watson, J., & Reavill, C. (2008). Muscarinic acetylcholine receptors as CNS drug targets. Pharmacology & therapeutics, 117(2), 232-243. DOI
  3. Freedman, S. B., Dawson, G. R., Iversen, L. L., Baker, R., & Hargreaves, R. J. (1993). The design of novel muscarinic partial agonists that have functional selectivity in pharmacological preparations in vitro and reduced side-effect profile in vivo. Life sciences, 52(5-6), 489-495. DOI
  4. Mikurova, A. V., Rybina, A. V., & Skvortsov, V. S. (2016). Prediction of selective inhibition of neuraminidase from various influenza virus strains by potential inhibitors. Biomeditsinskaya khimiya, 62(6), 691-703. DOI
  5. Thal, D. M., Sun, B., Feng, D., Nawaratne, V., Leach, K., Felder, C. C., ... & Kobilka, T. S. (2016). Crystal structures of the M1 and M4 muscarinic acetylcholine receptors. Nature, 531(7594), 335.
  6. Haga, K., Kruse, A. C., Asada, H., Yurugi-Kobayashi, T., Shiroishi, M., Zhang, C., ... & Kobayashi, T. (2012). Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature, 482(7386), 547.
  7. Kruse, A. C., Hu, J., Pan, A. C., Arlow, D. H., Rosenbaum, D. M., Rosemond, E., ... & Shaw, D. E. (2012). Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature, 482(7386), 552.
  8. Blundell, T., Carney, D., Gardner, S., Hayes, F., Howlin, B., Hubbard, T., ... & Sutcliffe, M. (1988). Knowledge?based protein modelling and design. European Journal of Biochemistry, 172(3), 513-520. DOI
  9. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., ... & Bourne, P. E. (2000). The protein data bank. Nucleic acids research, 28(1), 235-242. DOI
  10. SYBYL-X 2.1. Certara, Princeton, NJ, USA.
  11. Halgren, T. A. (1996). Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions. Journal of Computational Chemistry, 17(5/6), 520-552. DOI
  12. Scapecchi, S., Marucci, G., Matucci, R., Angeli, P., Bellucci, C., Buccioni, M., ... & Teodori, E. (2001). Structure–activity relationships in 2, 2-diphenyl-2-ethylthioacetic acid esters: unexpected agonistic activity in a series of muscarinic antagonists. Bioorganic & medicinal chemistry, 9(5), 1165-1174. DOI
  13. Alexander, S. P., Christopoulos, A., Davenport, A. P., Kelly, E., Marrion, N. V., Peters, J. A., ... & Southan, C. (2017). The Concise Guide to PHARMACOLOGY 2017/18: G protein-coupled receptors. British journal of pharmacology, 174(S1). DOI
  14. Dei, S., Bellucci, C., Buccioni, M., Ferraroni, M., Guandalini, L., Manetti, D., ... & Romanelli, M. N. (2007). Synthesis, affinity profile, and functional activity of muscarinic antagonists with a 1-methyl-2-(2, 2-alkylaryl-1, 3-oxathiolan-5-yl) pyrrolidine structure. Journal of medicinal chemistry, 50(6), 1409-1413. DOI
  15. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromolecule-ligand interactions. Journal of molecular biology, 161(2), 269-288. DOI
  16. Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., ... & Woods, R. J. (2005). The Amber biomolecular simulation programs. Journal of computational chemistry, 26(16), 1668-1688. DOI
  17. Massova, I., & Kollman, P. A. (2000). Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspectives in drug discovery and design, 18(1), 113-135. DOI
  18. Federal Research Center Computer Science and Control of Russian Academy of Sciences [Electronic resource]: site. - Moscow: FRC CS RAS.- URL: http://hhpcc.frccsc.ru (application date: 09/12/2018)