Molecular docking and QSAR analysis of a few Gama amino butyric acid aminotransferase inhibitors

Abdulfatai, Usman; Uzairu, Adamu; Shallangwa, Gideon Adamu

doi:10.1016/j.ejbas.2018.01.003

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Ligand number 33 t (Table 2) ring was found to be bound by interacting residues such as Phe401, Ile402, Arg422, Asp415, Thy416, and Lys442. Moreover, we realized that the binding scores generated were found to be better than one proposed by Abdulfatai and co-workers [18,19]. (Table 3) were designed and found to be more superior and better in term of binding strength than the one reported by Abdulfatai and co-workers [18,19].…”

Section: Resultsmentioning

confidence: 72%

Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

et al. 2019

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The concentrations of Gama aminobutyric acid (GABA) in the brain have been shown to be a major factor in the determinations of convulsions. Computational molecular docking study was carried out on the α_substituted acetamido-N-benzylacetamide (anticonvulsant agents) to complement our previous QSAR work with the help of Autodock vina version 4.0 of Pyrx software. Docking analysis revealed that all the compounds have better binding scores (with the highest binding score of − 13.8 kcal/mol) than the commercially sold antiepileptic drug vigabatrin (− 4.4 kcal/mol) but with the exception of ligands 2-acetamido-N-benzyl-2-hydroxyacetamide and 2-acetamido-N-benzyl-N-methyl-2-(pyrimidin-2-yl)acetamide which were revealed to have unpromising binding affinities. The most potent derivatives of α_substituted acetamido-N-benzylacetamide (2-acetamido-2-((3-aminophenyl)amino)-N-benzyl-N-methylacetamide with the experimental activity (pED 50) of 1.99) from our previous QSAR research was in agreement with this present work as the same compound was revealed to be having the highest binding affinity (− 13.8 kcal/mol). Moreover, three anticonvulsant compounds were designed and one of the compounds (2-acetamido-2-((3-amino-4-vinylphenyl)amino)-N-benzyl-N-methylacetamide) with best binding score of − 14.15 kcal/mol was found to have excellently docked with GABAAT enzyme through amino acid residues of Lys203, Pro347, Arg430, Thr353, Arg192 and Ala346 than the commercially sold vigabatrin (− 4.4 kcal/ mol). This study provides a valuable approach for pharmaceutical as well as medicinal chemists to synthesis these newly designed anticonvulsant drugs from α_substituted acetamido-N-benzylacetamide that will be more efficient in managing convulsion.

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Section: Resultsmentioning

confidence: 72%

Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

et al. 2019

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“…Shadow areas were also created via projecting the molecules along there two primary axes. Another descriptor included is the total solvents-accessible surface area 33,52,53,54 .…”

Section: Descriptorsmentioning

confidence: 99%

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2023

IJPSR

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Structure-Activity relationship (SAR) only identifies the chemical group responsible for producing the target biological effect in the organism. It was first present in 1865, structural activity relationship was refined dates back to the nineteenth century and now it has advanced from QSAR to 3D QSAR. Quantitative Structure-Activity Relationships (QSAR) are attempted to correlate the structural and biological properties of compounds or molecules. QSAR is used in drug design and medicinal chemistry. By using QSAR, scientists predefined toxicity related to organic molecules and determined the perfect potential biological activity that helps produce drug moiety. Group Based-Quantitative Structure-Activity Relationship (G-QSAR) is fragment dependent method which bases on molecule descriptors. Statistical parameters validate the GQSAR method. The cross-term fragment descriptor is used in GQSAR to study the relation of molecular fragments and variation in biological-based response. It provides a clue for designing new molecules and predicting their activity. In this article, we mainly concentrate on the various QSAR model such as Hansch Analysis, Free Wilson Analysis, various physicochemical properties, QSAR development process, QSAR model, and methodology of the GQSAR model, implementation and working as well as some general aspects in QSAR and GQSAR study.

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“…In the model equation, these variables negatively influence the value of the pIC50 response, so the value of pIC50 should increase with a decrease in the value of these molecular descriptors. The first descriptor consists of the two-dimensional descriptor minHBint4 [37], which is an atom-type electrotopological state that mainly encodes the minimum E-state descriptor of strength for potential hydrogen bonds of path length 4 in the molecule. However, the second descriptor Wlambdal.unity is a three-dimensional Weighted Holistic Invariant Molecular (WHIM) that encodes the atomic environment, weighted by unit weights.…”

Section: Analysis Of the Model With Oecd Regulatory Principlesmentioning

confidence: 99%

QSAR study of benzofuran and indole derivatives to predict new compounds as histone lysine methyl transferase inhibitors

2023

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Initiation and progression of several diseases by post-translational histone modifications are considered a worldwide problem. Enhancer of Zeste Homologue 2 (EZH2), which belongs to the histone-lysine N-methyl transferase (HKMT) family, has been emphasised as a promising target for cancer therapy. It is a major challenge for the scientific community to find novel approaches to treating this disease. In this study, a series of 51 derivatives of the benzofuran and indole families, previously experimentally evaluated against HKMT, was used to develop the best model with promising anticancer activity. The multiple linear regression (MLR) method, implemented in QSARINS software, was used with a genetic algorithm for variable selection. According to QSARINS, the model with two descriptors (minHBint4 and Wlambdal.unity) was found to be the best and its parameters fit well, and its validation was well established. The applicability domain was also validated for this model. Furthermore, its robustness (R2 = 0.9328), stability (Q2LOO = 0.9212, Q2LMO = 0.9187), and good predictive power (R2ext = 0.929) were also verified. Hence, this model was assumed to have predictive HKMT anticancer activity for designing active compounds. Molecular docking was also performed to identify binding interactions, and new molecules with better predicted biological activity (pIC50) were designed. The binding energy of the three designed compounds demonstrated higher binding activity at the target receptor, followed by complex stability, determined by a 100 ns molecular dynamics simulation and binding free energy calculation. Density functional theory (DFT) and pharmacokinetic analyses also confirmed their drug-like properties. Finally, it can be declared that the proposed tools allow rapid and economical identification of potential anti-HKMT drugs (anticancer drugs) for further development.

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Molecular docking and QSAR analysis of a few Gama amino butyric acid aminotransferase inhibitors

Cited by 5 publications

References 26 publications

Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

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QSAR study of benzofuran and indole derivatives to predict new compounds as histone lysine methyl transferase inhibitors

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