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MOLECULAR MODELLING, 3D-QSAR, AND DRUG DOCKING STUDIES ON THE ROLE OF NATURAL ANTICOAGULANT COMPOUNDS IN ANTITHROMBOTIC THERAPY.

ABSTRACT: Thromboembolic disorders are the leading cause of human mortality. Therefore, development of effective anticoagulant therapy is critical. Factor XIIIA (FXIIIA) protein is a crucial factor in the blood coagulation cascade, and hence it is a vital target for evolution of new antithrombotic agents. Structure-function studies of clotting factor active sites, clot formation, and thrombus structure have gained prominence in the efforts to develop novel anticoagulants. Factor XIIIA was homology modelled with the human transglutaminase-2 crystal structure as a base template for BLAST analysis. Docking and comparative binding site analysis revealed active site residue conservation and inhibitor-protein interactions. Nineteen small molecules possessing suspected anticoagulant properties were successfully docked into the FXIIIA active site following the best CoMFA and CoMSIA prediction values. Dabigatran etexilate was anticipated to be the best FXIIIA inhibitor among the nineteen anticoagulants with the highest binding affinity for the FXIIIA protein and the highest FlexX dock score of -29.8 KJ/mol. Structural properties of FXIIIA inhibitors with increased antithrombotic activity were predicted by this docking study.

SUBMITTER: Kakarla P

PROVIDER: S-EPMC4347951 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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MOLECULAR MODELLING, 3D-QSAR, AND DRUG DOCKING STUDIES ON THE ROLE OF NATURAL ANTICOAGULANT COMPOUNDS IN ANTITHROMBOTIC THERAPY.

Kakarla Prathusha P Devireddy Amith R AR Inupakutika Madhuri A MA Cheeti Upender R UR Floyd Jared T JT Mun Mukherjee M MM Vigil Raelyn N RN Hunter Russell P RP Varela Manuel F MF

International journal of pharmaceutical sciences and research 20140101 10

Thromboembolic disorders are the leading cause of human mortality. Therefore, development of effective anticoagulant therapy is critical. Factor XIIIA (FXIIIA) protein is a crucial factor in the blood coagulation cascade, and hence it is a vital target for evolution of new antithrombotic agents. Structure-function studies of clotting factor active sites, clot formation, and thrombus structure have gained prominence in the efforts to develop novel anticoagulants. Factor XIIIA was homology modelle ...[more]

PMID: 25750914

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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.

Dataset Information

MOLECULAR MODELLING, 3D-QSAR, AND DRUG DOCKING STUDIES ON THE ROLE OF NATURAL ANTICOAGULANT COMPOUNDS IN ANTITHROMBOTIC THERAPY.

Publications

MOLECULAR MODELLING, 3D-QSAR, AND DRUG DOCKING STUDIES ON THE ROLE OF NATURAL ANTICOAGULANT COMPOUNDS IN ANTITHROMBOTIC THERAPY.

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