Dataset Information

Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay.

ABSTRACT: In this study, pharmacophore based 3D QSAR models for human acetylcholinesterase (AChE) inhibitors were generated, with good significance, statistical values (r2training?=?0.73) and predictability (q2training?=?0.67). It was further validated by three methods (Fischer's test, decoy set and Güner-Henry scoring method) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The criteria for virtual screening were also validated by testing the selective AChE inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed a novel and selective AChE inhibitor. Thus, the findings reported herein may provide a new strategy for the discovery of selective AChE inhibitors. The IC50 value of compounds 5c and 6a presented selective inhibition of AChE without inhibiting butyrylcholinesterase (BChE) at uM level. Molecular docking studies were performed to explain the potent AChE inhibition of the target compounds studies to explain high affinity.

SUBMITTER: Jang C

PROVIDER: S-EPMC6175823 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay.

Jang Cheongyun C Yadav Dharmendra K DK Subedi Lalita L Venkatesan Ramu R Venkanna Arramshetti A Afzal Sualiha S Lee Eunhee E Yoo Jaewook J Ji Eunhee E Kim Sun Yeou SY Kim Mi-Hyun MH

Scientific reports 20181008 1

In this study, pharmacophore based 3D QSAR models for human acetylcholinesterase (AChE) inhibitors were generated, with good significance, statistical values (r2training = 0.73) and predictability (q2training = 0.67). It was further validated by three methods (Fischer's test, decoy set and Güner-Henry scoring method) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The cr ...[more]

PMID: 30297729

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Project description:Cyclophilin D (CypD) is a peptidyl prolyl isomerase F that resides in the mitochondrial matrix and associates with the inner mitochondrial membrane during the mitochondrial membrane permeability transition. CypD plays a central role in opening the mitochondrial membrane permeability transition pore (mPTP) leading to cell death and has been linked to Alzheimer's disease (AD). Because CypD interacts with amyloid beta (Aβ) to exacerbate mitochondrial and neuronal stress, it is a potential target for drugs to treat AD. Since appropriately designed small organic molecules might bind to CypD and block its interaction with Aβ, 20 trial compounds were designed using known procedures that started with fundamental pyrimidine and sulfonamide scaffolds know to have useful therapeutic effects. Two-dimensional (2D) quantitative structure-activity relationship (QSAR) methods were applied to 40 compounds with known IC50 values. These formed a training set and were followed by a trial set of 20 designed compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 with predicted values for both sets. Selectivity-determining descriptors were interpreted graphically in terms of principle component analyses. These descriptors can be very useful for predicting activity enhancement for lead compounds. A 3D pharmacophore model was also created. Molecular dynamics simulations were carried out for the 20 trial compounds with known IC50 values, and molecular descriptors were determined by 2D QSAR studies using the Lipinski rule-of-five. Fifteen of the 20 molecules satisfied all 5 Lipinski rules, and the remaining 5 satisfied 4 of the 5 Lipinski criteria and nearly satisfied the fifth. Our previous use of 2D QSAR, 3D pharmacophore models, and molecular docking experiments to successfully predict activity indicates that this can be a very powerful technique for screening large numbers of new compounds as active drug candidates. These studies will hopefully provide a basis for efficiently designing and screening large numbers of more potent and selective inhibitors for CypD treatment of AD.

Dataset Information

Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay.

Publications

Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay.

Similar Datasets

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