Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques

Hossain, Tabassum; Islam, Ataul; Pal, Ria; Saha, Achintya

doi:10.1007/s00044-013-0481-z

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…Female NSG mice were s.c. injected with 5-10×10 6 BT-474 or MCF-7 cells. Mice injected with MCF-7 cells were s.c. implanted on the same day with a 17β-estradiol (1 mg) beeswax pellet [ 48 ]. When average tumor volume reached 200 mm 3 , mice were randomized to treatment with vehicle, EHT1864 (100 mg/kg, i.p.…”

Section: Methodsmentioning

confidence: 99%

Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer

et al. 2017

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Rac GTPases have oncogenic roles in cell growth, survival, and migration. We tested response to the Rac inhibitor EHT1864 in a panel of breast cancer cell lines. EHT1864-induced growth inhibition was associated with dual inhibition of the PI3K/AKT/mTORC1 and MEK/ERK pathways. Breast cancer cells harboring PIK3CA mutations or HER2 overexpression were most sensitive to Rac inhibition, suggesting that such oncogenic alterations link Rac activation with PI3K/AKT/mTORC1 and MEK/ERK signaling. Interestingly, EHT1864 decreased activation of the mTORC1 substrate p70S6K earlier than AKT inhibition, suggesting that Rac may activate mTORC1/p70S6K independently of AKT. Comparison of the growth-inhibitory profile of EHT1864 to 137 other anti-cancer drugs across 656 cancer cell lines revealed significant correlation with the p70S6K inhibitor PF-4708671. We confirmed that Rac complexes contain MEK1/2 and ERK1/2, but also contain p70S6K; these interactions were disrupted by EHT1864. Pharmacokinetic profiles revealed that EHT1864 was present in mouse plasma at concentrations effective in vitro for approximately 1 h after intraperitoneal administration. EHT1864 suppressed growth of HER2+ tumors, and enhanced response to anti-estrogen treatment in ER+ tumors. Further therapeutic development of Rac inhibitors for HER2+ and PIK3CA-mutant cancers is warranted.

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Section: Methodsmentioning

confidence: 99%

Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer

et al. 2017

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“…The established procedures for QSAR are utilized predominantly in cheminformatics, drug discovery and to estimate the biological processes of unique biochemical principles, besides the pharmacokinetic assessments of specific chemicals [ 27 , 28 ]. There are several computational analyses documented for the detection of new components in the management of AD [ 29 , 30 , 31 , 32 ], but there is still no precise treatment for AD [ 29 ]. Previously, QSAR models have been developed for inhibitors targeting AChE, BChE and MAO enzymes.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Ojo

Okolie

et al. 2021

Molecules

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Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

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“…The chemometric models for binding affinity to BACE have been already reported. 42 In the present work, important bio-phoric features required for BACE inhibitory activity have been explored using various ligand-based chemometric tools, followed by validation by structure-based docking analysis. The de novo designed molecules were screened to expedite the process of identifying potential BACE inhibitors.…”

Section: Resultsmentioning

confidence: 99%

Chemometric design to explore pharmacophore features of BACE inhibitors for controlling Alzheimer's disease

2015

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The β-amyloid precursor protein cleavage enzyme (BACE) has been conceived to be an attractive therapeutic target to control Alzheimer's disease (AD). Validated ligand-based pharmacophore mapping was combined with 3D QSAR modeling approaches that include CoMFA, CoMSIA and HQSAR techniques to identify structural and physico-chemical requirements for a potential BACE inhibitor using a database containing 980 structurally diverse compounds, assembled from different reports. A structure-based docking technique was also used to validate the features obtained from the ligand-based models, which were further used to screen the database of compounds designed by a de novo approach. Contour maps of 3D QSAR models, CoMFA (R(2) = 0.880, se = 0.402, Q(2) = 0.596, Rpred(2) = 0.713,) and CoMSIA (R(2) = 0.903, se = 0.362, Q(2) = 0.578, Rpred(2) = 0.715), and a pharmacophore space model (R(2) = 0.833, rmsd = 1.578, Q(2) = 0.845, Rpred(2) = 0. 764) depict that the models are robust and provide an explanation of the important features such as steric, electrostatic, hydrophobic, positive ionization, hydrogen bond acceptor and donor properties, which play important roles for interaction with the receptor site cavity. The HQSAR study (R(2) = 0.823, se = 0.488, Q(2) = 0.823, Rpred(2) = 0.768) and de novo design, which generate new fragments, illustrated the important molecular fingerprints for inhibition. The docking study elucidated the important interactions between the amino acid residues (Gly11, Thr72, Asp228, Gly230, Thr231, Arg235) at the catalytic site of the receptor and the ligand, indicating the structural requirements of the inhibitors. The de novo designed molecules were further screened for ADMET properties, and ligand-receptor interactions of the top hits were analysed by molecular docking to explore pharmacophore features of BACE inhibitors.

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Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques

Cited by 11 publications

References 26 publications

Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer

Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Chemometric design to explore pharmacophore features of BACE inhibitors for controlling Alzheimer's disease

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