Biomedical Chemistry: Research and Methods, 2018, 1(3), e00072
The 40th Anniversary of the Institute of Physiologically Active Compounds of the Russian Academy of Sciences

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

V. Mikurova1,2*, V. S. Skvortsov1,2, O. A. Raevsky1

1Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia,*e-mail:
2Institute of Biomedical Chemistry, 10 Pogodinskaya Street, Moscow, 119121, Russia

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

DOI: 10.18097/BMCRM00072

The whole version of this paper is available in Russian.

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

Figure 1. The structure of human M2 acetylcholine muscarinic receptor. Chimeric part is marked by red. The binding site located at a considerable distance from the chimeric part.

Figure 2. Comparison of sequences of human and rat acetylcholine muscarinic receptors. Differences are marked by red. The chimeric part is underlined.

Figure 3. Prediction of meaning for dataset 2 (red) by model, learned with dataset 1. Receptor M1.

Figure 4. Prediction for receptors M1, M2 and M4 with model learned by dataset of M2.

Table 1. Strucrures of acetylcholine muscarinic receptors available in PDB.

Table 2. Antagonists of acetylcholine muscarinic receptors.

Table 3. Learning parameters for linear regression models for particular receptors.

Table 4. Parameters of models for cross-receptors prediction (R2).


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


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