Biomedical Chemistry: Research and Methods 2020, 3(3), e00129

Generalized Predictive Model of Estimation of Inhibition of Muscarinic Receptors M1-M5

V. Mikurova1*, V. S. Skvortsov1,2, V. V. Grigoryev2

1Institute of Biomedical Chemistry, 10 Pogodinskaya Street, Moscow, 119121, Russia

2Institute of Physiologically Active Compounds, 1 Severniy pr., Chernogolovka, 142432, Russia; *e-mail: a.v.mikurova@ibmc.msk.ru

Keywords: acetylcholine muscarinic receptors; inhibitors; comparative inhibition; docking; computational methods; molecular dynamics; QSAR

Keywords: acetylcholine muscarinic receptors; inhibitors; comparative inhibition; docking; computational methods; molecular dynamics; QSAR

DOI:10.18097/BMCRM00129

The whole version of this paper is available in Russian.

A general predictive model for assessing the inhibition constant (Ki) value of human acetylcholine muscarinic receptors M1-M5 by potential ligands has been constructed. We used information on the three-dimensional structure of human M1, M2, M4, and M5 receptors, as well as a model of the M3 receptor constructed according to homology based on the structure of the rat M3 receptor. A set of complexes of known inhibitors with the target receptor constructed by means of molecular docking, was selected using an additional option: the coincidence of the spatial position of 4 pharmacophore points of a tested inhibitor and tiotropium, for which the position in the crystal structure was known. For five types of M receptors 199 complexes with known Ki values were selected. Based on the data obtained during molecular dynamics simulation of these complexes by means of the MM-PBSA/MM-GBSA methods, their energy characteristics were calculated. They were used as independent variables in linear regression equations for pKi value prediction. The R2 prediction for the generalized equation was 0.7, and the mean prediction error was 0.55 logarithmic units with a range for pKi.= 4.7.
Figure 1. The Tiotropium structure with highlighted pharmacophore points (A) and examples of structures (B, C) and common elements (D-F) for structures with known Ki values for M1-M5 cholinergic receptors.
Figure 2. The comparison of observed and predicted pKi values in testing procedure.
A. The prediction for set M2 by the equation obtained for set M1, M3, M4, M5.
B. The prediction for set M3 by the equation obtained for set M1, M2, M3, M5.
C. The prediction for even half of the set M1, M2, M3, M4, M5 by the equation obtained for odd part.
D. The prediction for odd half of the set M1, M2, M3, M4, M5 by the equation obtained for even part.

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Table 1. Strucrures of acetylcholine muscarinic receptors available in PDB [4].

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Table 2. The parameters of training and testing linear regression models on sets for individual receptors and their combinations.

FUNDING

The work was carried out within the framework of the state task for 2018 (topic number 0090-2017-0020). The software porting on hybrid Power 9 cluster was supported by the Russian Foundation for Basic Research (project 18-29-03100).

SUPPLEMENTARY

Supplementary materials are available at http://dx.doi.org/10.18097/BMCRM00129

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