A set of linear regression equations predicting the IC50 values for SARS-CoV-2 main protease inhibitors was analyzed. For 180 competitive inhibitors, we have simulated the molecular dynamics of enzyme-inhibitor complexes with known structures or modeled using molecular docking. In the docking procedure, the selection of final poses was restricted by similarity to known structural analogs. The values of the energy contributions obtained by means of calculation of the free energy change of the enzyme-inhibitor complex performed by two variants of the MMPBSA (MMGBSA) method and a number of physicochemical characteristics of the inhibitors were used as independent variables. During the learning process, indicator variables were used for inhibitor subsets obtained from various literature sources to compensate the existing systematic deviations from the target value. A leave one out and leave 20% out cross validation procedures were used to evaluate the prediction quality. For the total logarithmic range width of 3.71, the mean error in predicting the lg(IC50) value was 0.45 log units. The stability of the prediction depending on the variability of the complex in molecular dynamics was investigated.
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Keywords: SARS-CoV-2, main protease, competitive inhibitors, QSAR
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Ivanova Ya.O., Skvortsov V.S. (2023) The prediction of main protease SARS-CoV-2 inhibition based on models of enzyme-inhibitor complexes. Biomeditsinskaya Khimiya, 69(5), 322-327.
Ivanova Ya.O. et al. The prediction of main protease SARS-CoV-2 inhibition based on models of enzyme-inhibitor complexes // Biomeditsinskaya Khimiya. - 2023. - V. 69. -N 5. - P. 322-327.
Ivanova Ya.O. et al., "The prediction of main protease SARS-CoV-2 inhibition based on models of enzyme-inhibitor complexes." Biomeditsinskaya Khimiya 69.5 (2023): 322-327.
Ivanova, Ya. O., Skvortsov, V. S. (2023). The prediction of main protease SARS-CoV-2 inhibition based on models of enzyme-inhibitor complexes. Biomeditsinskaya Khimiya, 69(5), 322-327.
References
Ivanova, Ya. O., Voronina, A. I., Skvortsov, V. S. (2022). The prediction of SARS-CoV-2 main protease inhibition with filtering by position of ligand. Biomeditsinskaya Khimiya, 68(6), 444-458. CrossRef Scholar google search
Han S.H., Goins C.M., Arya T., Shin W.-J., Maw J., Hooper A., Sonawane D.P., Porter M.R., Bannister B.E., Crouch R.D., Lindsey A.A., Lakatos G., Martinez S.R., Alvarado J., Akers W.S., Wang N.S., Jung J.U., Macdonald J.D., Stauffer S.R. (2022) Structure-based optimization of ML300-derived, noncovalent inhibitors targeting the severe acute respiratory syndrome coronavirus 3CL protease (SARS-CoV-2 3CL(pro)). J. Med. Chem., 65(4), 2880-2904. CrossRef Scholar google search
Case D.A., Aktulga H.M., Belfon K., Ben-Shalom I.Y., Berryman J.T., Brozell S.R., Cerutti D.S., Cheatham T.E. III, Cisneros G.A., Cruzeiro V.W.D., Darden T.A., Forouzesh N., Giambaєu G., Giese T., Gilson M.K., Gohlke H., Goetz A.W., Harris J., Izadi S., Izmailov S.A., Kasavajhala K., Kaymak M.C. et al (2023) Amber 2023, University of California, San Francisco. Scholar google search
Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E. (2000) The protein data bank. Nucleic Acids Res., 28, 235-242. CrossRef Scholar google search
Schrodinger (Schrodinger, LLC, New York, NY). Retrieved September 02, 2023 from https://www.schrodinger.com/. Scholar google search
Harder E., Damm W., Maple J., Wu C., Reboul M., Xiang J.Y., Wang L., Lupyan D., Dahlgren M.K., Knight J.L., Kaus J.W., Cerutti D.S., Krilov G., Jorgensen W.L., Abel R., Friesner R.A. (2015) OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput., 12(1), 281-296. CrossRef Scholar google search
Mikurova A.V., Skvortsov V.S., Grigoryev V.V. (2020) Generalized predictive model of estimation of inhibition of muscarinic receptors M1-M5. Biomedical Chemistry: Research and Methods, 3(3), e00129. CrossRef Scholar google search
Massova I., Kollman P.A. (2000) Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspectives Drug Discovery Design, 18, 113-135. CrossRef Scholar google search
Mikurova A.V., Skvortsov V.S. (2018) Prediction of progestin affinity for the human progesterone receptor based on corrected RBA data. Biomedical Chemistry: Research and Methods, 1(4), e00080. CrossRef Scholar google search
Gentile F., Fernandez M., Ban F., Ton A.-T., Mslati H., Perez C.F., Leblanc E., Yaacoub J.C., Gleave J., Stern A., Wong B., Jean F., Strynadka N., Cherkasov A. (2021) Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus deep docking of 40 billion small molecules. Chemical Science, 12(48), 15960-15974. CrossRef Scholar google search
Deodato D., Asad N., Dore T.M. (2022) Discovery of 2-thiobenzimidazoles as noncovalent inhibitors of SARS-CoV-2 main protease. Bioorganic Med. Chem. Lett., 72, 128867. CrossRef Scholar google search
