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Molecular modelling studies of quinazolinone derivatives as MMP-13 inhibitors by QSAR, molecular docking and molecular dynamics simulations techniques.

ABSTRACT: Matrix metalloproteinase-13 (MMP-13) is an attractive drug target for the treatment of osteoarthritis (OA). In this study, a series of quinazolinone derivatives as MMP-13 inhibitors were firstly systematically studied using QSAR, molecular docking and molecular dynamics (MD) simulation. The reliable CoMFA (q 2 = 0.646, r 2 = 0.992, R pred 2 = 0.829) and CoMSIA (q 2 = 0.704, r 2 = 0.992, R pred 2 = 0.839) models were constructed and verified by the Topomer CoMFA model. Results of contour maps indicated that the electrostatic, hydrophobic and H-bond acceptor fields primarily influenced the activity of MMP-13 inhibitors in the models. Several key residues (Ala238, Thr245, Thr247, Met253, Asn215 and Lys140) were identified as important factors to improve the activity and stability of the inhibitor through hydrogen bonding and electrostatic interaction. Based on these results, eight novel quinazolinones (D1-D8) were further designed. Additionally, all designed compounds showed good pharmacokinetic properties by ADMET predictions. Compounds D3 and D8 exhibited excellent predictive activity, and the 10 ns MD simulations analysis revealed that the hydrogen bonding interaction with residues (Ser250 and Gly248) was enhanced, and the small group in R2 and U-shaped conformation was of pivotal importance. These results provided strong guidance for the discovery and design of novel potential MMP-13 inhibitors.

SUBMITTER: Huang S

PROVIDER: S-EPMC6350765 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Project description:Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR ( Rtrain2 =0.8414, Q2 = 0.7680) and GFA-ANN ( Rtrain 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES ( Rtrain 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA ( Rtrain2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

Project description:BackgroundInfluenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).ResultsThe 2D-QSAR modeling results showed GFA-MLR ( Rtrain2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, Rtest2 = 0.8943, Rpred2 = 0.7745) and GFA-ANN ( Rtrain2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, Rtest2 = 0.8831, Rpred2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( Rtrain2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( Rtrain2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.ConclusionThe drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

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Project description:The diverse pharmacological role of dihydropyrimidinone scaffold has made it to be an interesting drug target. Because of the high incidence and mortality rate of breast cancer, there is a dire need of discovering new pharmacotherapeutic agents in managing this disease. A series of twenty-two derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3a-3k) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4a-4k) synthesized in a previous study were evaluated for their anticancer potential against breast cancer cell line. Molecular docking studies were performed to analyze the binding mode and interaction pattern of these compounds against nine breast cancer target proteins. The in vitro cell proliferation assay was performed against the breast cancer cell line MCF-7. The structure activity relationship of these compounds was further studied using QSARINS. Among nine proteins, the docking analysis revealed efficient binding of compounds 4f, 4e, 3e, 4g, and 4h against all target proteins. The in vitro cytotoxic assay revealed significant anticancer activity of compound 4f having IC50 of 2.15 μM. The compounds 4e, 3e, 4g, and 4h also showed anticancer activities with IC50 of 2.401, 2.41, 2.47 and 2.33 μM, respectively. The standard tamoxifen showed IC50 1.88 μM. The 2D qualitative structure-activity relationship (QSAR) analysis was also carried out to identify potential breast cancer targets through QSARINS. The final QSAR equation revealed good predictivity and statistical validation R 2 and Q 2 values for the model obtained from QSARINS was 0.98 and 0.97, respectively. The active compounds showed very good anticancer activities, and the binding analysis has revealed stable hydrogen bonding of these compounds with the target proteins. Moreover, the QSAR analysis has predicted useful information on the structural requirement of these compounds as anticancer agents with the importance of topological and autocorrelated descriptors in effecting the cancer activities.

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Molecular modelling studies of quinazolinone derivatives as MMP-13 inhibitors by QSAR, molecular docking and molecular dynamics simulations techniques.

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