Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling

Taha, Mutasem O.; Qandil, Amjad M.; Zaki, Dhia D.; AlDamen, Murad A.

doi:10.1016/j.ejmech.2004.10.014

Cited by 51 publications

(64 citation statements)

References 70 publications

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“…[54] Restricting the explored pharmacophoric space should improve the efficiency of optimization by allowing effective evaluation of a limited number of pharmacophoric models. [59] However, imposing more limitations on the "pharmacophoric space" might reduce the possibility of converging upon optimal pharmacophoric hypotheses, particularly if they occur outside the "boundaries" of the pharmacophoric space. [59] Therefore, it was decided to explore the pharmacophoric space of DPP IV inhibitors under reasonably imposed "boundaries" through four carefully selected training subsets from the collected inhibitors: subsets A, B, C, and D (Table 1) …”

Section: Wwwchemmedchemorgmentioning

confidence: 99%

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Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening

2008

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Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

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Section: Wwwchemmedchemorgmentioning

confidence: 99%

“…CATALYST pharmacophores have been used as 3D queries for database searching and in 3D-QSAR A C H T U N G T R E N N U N G studies. [52][53][54][58][59][60][61][62] Data mining and conformational coverage…”

Section: Introductionmentioning

confidence: 99%

Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening

2008

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“…3 Traditionally, research has been focused on the inhibition of thrombin, but lately much effort has been directed towards the inhibition of the enzyme factor Xa. 1,4 Factor Xa (fXa) is a serine protease that converts prothrombin to thrombin, which then catalyses the reactions that lead to the production of blood clots. Therefore, by inhibiting fXa, the blood coagulation cascade can be halted.…”

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confidence: 99%

“…Several sets of fXa inhibitors have been previously studied with rigorous free energy methods, 3,25,26 LIE, 3,27 empirical scoring functions, 28,29 and QSAR. 4 We have chosen to study a set of 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors, for which both accurate binding data and crystal structures are available. 2 We show how the protocol for performing TI can be systematically tuned to decrease the computational demand, while still retaining a high accuracy and precision.…”

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confidence: 99%

Binding Affinities of Factor Xa Inhibitors Estimated by Thermodynamic Integration and MM/GBSA

Genheden

Nilsson

Ryde

2011

J. Chem. Inf. Model.

167

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We present free energy estimates of nine 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors of factor Xa. Using alchemical thermodynamic integration (TI) calculations, we estimate the difference in binding free energies with high accuracy and precision, except for mutations involving one of the amidinobenzyl rings. Crystal studies show that the inhibitors may bind in two distinct conformations and using TI, we show that the two conformations give a similar binding affinity. Furthermore we show that we can reduce the computational demand, while still retaining a high accuracy and precision, by using fewer integration points and shorter protein-ligand simulations. Finally, we have compared the TI results to those obtained with the simpler MM/GBSA method (molecular-mechanics with generalised Born surface-area solvation). MM/GBSA gives better results for the mutations that involve a change of net charge, but if a precision similar to that of the TI method is required, the MM/GBSA method is actually slightly more expensive. Thus, we have shown that TI could be a valuable tool in drug design.2 Introduction Myocardial infarction, pulmonary embolism, and stroke are among the leading causes of death in the industrialised parts of the world. They are triggered by undesirable clot formation within blood vessels. The clot formation is initiated by an intricate series of enzymatic reactions that eventually generate fibrin.

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“…Factor X is an enzyme, a serine endopeptidase, synthesized in liver which plays a key role at several stages of the coagulation system. The most commonly used anticoagulants in clinical practice, warfarin and the heparin series of anticoagulants and fondaparinux, act to inhibit the action of Factor Xa to various extent (Mendel et al, 2007;Nazar et al, 2004a, b;Li et al, 2006;Quan et al, 2006;Taha et al, 2005;Lin et al, 2007;Bhongade et al, 2005). However, the new drugs are still far from fulfilling the desired objectives and hence development of newer agents with properties closer to that of an ideal anticoagulant remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

3D QSAR, docking studies, and pharmacophore modeling of selected factor Xa inhibitors

Choudhari

Bhatia

2011

Med Chem Res

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Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling

Cited by 51 publications

References 70 publications

Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening

Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening

Binding Affinities of Factor Xa Inhibitors Estimated by Thermodynamic Integration and MM/GBSA

3D QSAR, docking studies, and pharmacophore modeling of selected factor Xa inhibitors

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