The application of PASS-computer program and molecular docking for the search of new anticonvulsants
Keywords:
docking, anticonvulsant activity, PASS computer program, di (three) azaheterocycle.Abstract
Introduction. Currently the priority goal of designing drugs is the integration of the methods of organic chemistry and pharmacology. The application of computer programmes which can predict interaction of potential drugs with molecules of biological targets makes possible to decrease the number of experiments on laboratory animals. Thereby the economic efficiency of production of new medicines increases. Models of the research the anticonvulsant activity (in particular, korazol, thiosemikarbazid, strychnine, etc.) are the most rigid experimental models of pharmacological screening, which basically entails the pains of laboratory animals or their death. The application of computer programmes in the research of potential anticonvulsants has economic and social desirability and high level of importance for the pharmaceutical science and health care. The most perspective methods of research are the virtual screening, molecular docking. These methods allow to evaluate the affinity of a substance to a specific biological target, i.e. to identify an inhibitor of a particular enzyme or protein. Material and methods. We have carried out the construction of 50 groups substances (507 hypothetical structures). We have chosen the five-membered di(three)azaheterocycle as basic pharmacophores to form virtual structures because firstly their structure is similar to cyclic conformation of neurotransmitter and secondly according to the literature perspective anticonvulsants had already found among these derivatives. Computer prediction of pharmacological activity for all compounds of virtual database was performed using the PASS (Prediction of Activity Spectra for Substances) computer programme. Results obtained by PASS-computer programme showed prospects of search the anticonvulsants among 10 groups of derivatives di(three)azaheterocycles (probable activity (Pa) of substances of these groups are from 0.5 to 0.84). In order to determine the potential anticonvulsant activity of 1,2,3(1,2,4)triazole, 1,3,4-oxa(thia)diazole we investigated the mechanisms of action that involve the interaction of the ligand NMDA-, GAMKA- or glutamate receptors and GABA-AT ligand-enzyme. We have perfomed docking research for our structures and for known anticonvulsants using the Fast Dock method, in which both protein and ligand are rigid (Software SCIGRESS; Fujitsu, Fukuoka, Japan). We have evaluated affinity of the investigated structures with molecules biotargets: GABAA receptor protein (PDB code 1GNU), glutamate receptor protein Glu-1 (PDB code 1EWK), GluN1 NMDA receptor protein (PDB code 3Q41) and protein enzyme GABA-AT (PDB code 1OHW). Results and discussion. As a result, we have obtained the values of scoring functions Consensus, which enabled to evaluate affinity compounds and biological anticonvulsant targets and identify 11 perspective groups of compounds (number of compounds 190) that can selectively inhibit NMDA, a GABA - or glutamate receptors and GABA aminotransferase enzyme in comparison with known anticonvulsant drugs. The number of active groups of the results PASS prediction according to the obtained results is 10 (the number of compounds 168). It should be noted that result of docking research coincided with the results of PASS prediction for eight groups of compounds. Conclusion. 1. Eleven groups of compounds derivatives of 1,2,3(1,2,4) -triazols, 1,3,4-oxadiazoles and 1,3,4- thiadiazoles was selected for further screening as perspective anticonvulsants; 2. GABA-ergic mode of action for 8 groups of derivatives and glutamatergic mode of action for 3 groups of derivatives five-membered di(three)azaheterocycle was predicted.
References
Dimitrov S., Dimitrova N., Parkerton T., / Base-line model for identifying the bioaccumulation potential of chemicals, SAR QSAR Environ. Res. -2005.-16. P. -531–554
GABAa and GABAc Receptors: Molecular Modelling and QSAR Analysis of Selective Ligands /Palyulin V.A., Chupakhin V.I., Osolodkin D.I., Radchenko E.V., Zefirov N.S. //17th European Symposium on Quantitative Structure–Activity Relationships. Uppsala, Sweden. 2008. P.29–34
Oprea T. I. Is there a difference between leads and drugs? A historical perspective / T. I. Oprea, A. M. Davis, S. J. Teague // J. Chem. Inf. Comput. Sci. – 2001. – Vol. 41, № 11. – P. 1308–1315.
Triazolines – XXVII. ∆2 – 1,2,3–triazoline anticonvulsants: Novel „built–in” heterocyclic prodrugs with a unique „dual–action” mechanism for imparing excitatory amino acid L–glutamate neurotransmission / P. K. Kadaba, P. J. Stevenson, I. Pnane et al. // Bioorg. Med. Chem. – 1996. – Vol. 4, № 2.– С. 165–178.
Kadaba PK, Dixit T. Aminoalkylpyridines (AAPs), triazoline metabolite analogues, as anticonvulsants highly effective in the MES test. //Curr Med Chem. –2003 -10(20) . – P. 2109–2021.
1,5-Diarylsubstituted 1,2,3-triazoles as potassium channel activators. VI. / Biagi G., Calderone V., Giorgi I. // Farmaco. – 2004. – Vol. 59, № 5. – P. 397-404.
Filimonov D.A., Poroikov V.V., in: Chemoinformatics Approaches to Virtual Screening.
Alexandre Varnek and Alexander Tropsha, Eds. Cambridge (UK): RSC Publishing.- 2008. P.182-216.
Lagunin A., Zakharov A., Filimonov D., Poroikov V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Molecular Informatics.- 2011.- 30, № (2-3). P.241–250.
Anzali S. Discriminating between drugs and nondrugs by prediction of activity spectra for substances (PASS) / S.Anzali, G.Barnickel, В.Cezanne et al. // J. Med. Chem. – 2001. – Vоl.4, № 16. – P. 2432 - 2437.
Victoriya Georgiyants. Synthesis, docking studies, and biological evaluation of anti-ulcer activity of 4-allyl-5-(4-R1)-phenylthiomethyl-1,2,4-triazole-3-ilmercaptoacetic acid derivatives/ Victoriya Georgiyants, Lina Perekhoda, Narzullо Saidov at all // Eur. Chem. Bull.– 2014.–Vol.3, №5 .–P. 466-471
Poroikov V. Computer-aided prediction of biological activity spectra. Application for finding and optimization of new leads. Rational Approaches to Drug Design / Poroikov V., Filimonov D. // Ed. by H.-D. Holtje, W.Sippl. – Barcelona: Prous Science, 2001. – P. 403 – 407.
Knight D. The X–ray crystal structure and putative ligand–derived peptide binding properties of gamma–aminobutyric acid receptor type A receptor–associated protein/ D.Knight, R Harris, MS McAlister et al. // J Biol Chem.– 2002. – Vol. 277, №7. – P. 5556–5561.
Kunishima N. Crystal structure of metabotropic glutamate receptor subtype 1 complexed with glutamate Structural basis of glutamate recognition by a dimeric metabotropic glutamate receptor./ Kunishima N., Shimada Y., Tsuji Y et al. // Nature.– 2000. – Vol. 407. – P. 971–977.
Storici P. Structures of gamma–aminobutyric acid (GABA) aminotransferase, a pyridoxal 5'–phosphate, and [2Fe–2S] cluster–containing enzyme, complexed with gamma–ethynyl–GABA and with the antiepilepsy drug vigabatrin /Storici P., De Biase D., Bossa, F et al..// J.Biol.Chem.– 2004. – Vol. 279. – P. 36.
Michael D. Clift, Richard B. Silverma. Synthesis and Evaluation of Novel Aromatic Substrates and Competitive Inhibitors of GABA Aminotransferase // Bioorg Med Chem Lett.– 2008. – Vol. 18, №10. – P. 3122–3125.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2020 Annals of Mechnikov's Institute
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported License.
