МЕТОД ПРОГНОЗА АНТИГЛИКИРУЮЩЕЙ АКТИВНОСТИ ПОСРЕДСТВОМ ОПРЕДЕЛЕНИЯ ЭНЕРГИЙ ГРАНИЧНЫХ МОЛЕКУЛЯРНЫХ ОРБИТАЛЕЙ НА ПРИМЕРЕ НОВЫХ 4-ГИДРОКСИ-1,4-ДИГИДРОАЗОЛО[5,1-С]-1,2,4-ТРИАЗИНОВ

Translated title of the contribution: PREDICTION OF CHEMICAL COMPOUNDS' ANTIGLYCATION ACTIVITYBY THE FRONTIER MOLECULAR ORBITALS' ENERGIES DETERMINATIONON THE EXAMPLE OF AZOLOTRIAZINE DERIVATIVES INVESTIGATION

Р. А. Литвинов, Роман Александрович Дрокин, Д. Д. Шамшина, М. Ю. Каленова, Л. Э. Усмиянова, Е. А. Муравьева, Павел Михайлович Васильев, Егор Константинович Воинков, Евгений Нарциссович Уломский, Александр Алексеевич Спасов, Владимир Леонидович Русинов

Research output: Contribution to journalArticle

Abstract

Protein glycation and formation of advanced glycation end products (AGEs) play an important role in the pathogenesis of diabetes mellitus (DM) complications, neurodegenerations and age-related diseases. Prediction model for antiglycation activity can reduce costs and increase productivity and quality of screening investigations. Azolo[5,1-c][1,2,4]triazines and azolo[1,5-a]pyrimidines are well known biologically active compounds, which additionally have antiglycation properties. Hence, a number of 4-hydroxy-4H-azolo-1,4-dihydro [5,1-s]-1,2,4-triazines were selected for the prediction model creating. It has been established that azolotriazine derivatives have an anti-glycation effect, inhibiting a glycation of bovine serum albumin (BSA) by glucose with equal or greater activity than aminoguanidine. The activity range at 1000 μM concentration for variously substituted derivatives is 23.0–71.6% (30.3 ± 1.2% for aminoguanidine). The highest activity is detected for (4-hydroxy-4H-3-cyano-triazolo-1,4-dihydro[5,1-s]-1,2,4-triazines). The levels of antiglycation activity for the compounds (excluding aminoguanidine) correlate with the magnitude of the values of difference between HOMO and LUMO energies (∆(HOMO-LUMO), (HOMO – highest occupied molecular orbital, LUMO – lowest unoccupied molecular orbital), established by PM3 semi-empirical method. Using the method of artificial neural network modeling, a mathematical model for describing the dependence of antiglycation activity on the calculated energies is obtained. It has been established that the ELUMO and ∆(HOMO-LUMO) energies have the largest contribution to the activity. The model can be used for the prediction of antiglycation activity.
Translated title of the contributionPREDICTION OF CHEMICAL COMPOUNDS' ANTIGLYCATION ACTIVITYBY THE FRONTIER MOLECULAR ORBITALS' ENERGIES DETERMINATIONON THE EXAMPLE OF AZOLOTRIAZINE DERIVATIVES INVESTIGATION
Original languageRussian
Pages (from-to)784-791
Number of pages8
JournalБиоорганическая химия
Volume46
Issue number6
DOIs
Publication statusPublished - 2020

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