Dual Allosteric Effect in Glycine/NMDA Receptor Antagonism: A Comparative QSAR Approach

Sharma, Manish; Gupta, Vipul

doi:10.3390/ph3103167

Cited by 8 publications

(4 citation statements)

References 39 publications

(42 reference statements)

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“…For protein 1PBQ, crucial interacting residues with co crystallised ligand DCKA are Pro124, Ser180, Thr126 and Arg131. Hydrophobic pocket of 1PBQ has following amino acid residues: Phe16, Phe92, Trp223 and Phe250 [ 50 ]. Anisopirol showed the best docking score of − 6.903 and glide energy of − 36.533 kcal/mol whereas, DCKA showed a docking score of − 14.084 and glide energy of − 43.865 kcal/mol.…”

Section: Resultsmentioning

confidence: 99%

In silico repurposing of antipsychotic drugs for Alzheimer’s disease

2017

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BackgroundAlzheimer’s disease (AD) is the most prevalent form of dementia and represents one of the highest unmet requirements in medicine today. There is shortage of novel molecules entering into market because of poor pharmacokinetic properties and safety issues. Drug repurposing offers an opportunity to reinvigorate the slowing drug discovery process by finding new uses for existing drugs. The major advantage of the drug repurposing approach is that the safety issues are already investigated in the clinical trials and the drugs are commercially available in the marketplace. As this approach provides an effective solution to hasten the process of providing new alternative drugs for AD, the current study shows the molecular interaction of already known antipsychotic drugs with the different protein targets implicated in AD using in silico studies.ResultA computational method based on ligand–protein interaction was adopted in present study to explore potential antipsychotic drugs for the treatment of AD. The screening of approximately 150 antipsychotic drugs was performed on five major protein targets (AChE, BuChE, BACE 1, MAO and NMDA) by molecular docking. In this study, for each protein target, the best drug was identified on the basis of dock score and glide energy. The top hits were then compared with the already known inhibitor of the respective proteins. Some of the drugs showed relatively better docking score and binding energies as compared to the already known inhibitors of the respective targets. Molecular descriptors like molecular weight, number of hydrogen bond donors, acceptors, predicted octanol/water partition coefficient and percentage human oral absorption were also analysed to determine the in silico ADME properties of these drugs and all were found in the acceptable range and follows Lipinski’s rule.ConclusionThe present study have led to unravel the potential of leading antipsychotic drugs such as pimozide, bromperidol, melperone, anisoperidone, benperidol and anisopirol against multiple targets associated with AD. Benperidol was found to be the best candidate drug interacting with different target proteins involved in AD.

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Section: Resultsmentioning

confidence: 99%

In silico repurposing of antipsychotic drugs for Alzheimer’s disease

2017

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“…Numerous docking studies have been previously performed based on a single (crystal or homology) protein structure1920212223242526. Nevertheless, NMDARs are highly flexible per se as illustrated by published crystal structures, namely GluN1 in co-complexes with glycine (PDB code: 1PB7), 5,7-dichlorokynurenic acid (DCKA) (PDB code: 1PBQ), and cycloleucine (PDB code: 1Y1M)2728.…”

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confidence: 99%

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

Leong

Syu

Ding

et al. 2017

Sci Rep

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The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928–0.988, = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967, = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

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“…Activities of the central nervous system (CNS) are being controlled by excitatory and inhibitory amino acids which are known as neurotransmitters [1]. The balance between the excitatory and inhibitory are needed to maintain a healthy life whereas an imbalance in the two systems (excitatory and inhibitory) has been implicated in various mental disorders.…”

Section: Introductionmentioning

confidence: 99%

“…The balance between the excitatory and inhibitory are needed to maintain a healthy life whereas an imbalance in the two systems (excitatory and inhibitory) has been implicated in various mental disorders. Receptors for neurotransmitters are classified as ionotropic or metabotropic [1,2]. N-methyl-D-aspartic acid (NMDA) are excitatory ionotropic receptor that allows the passage of ions such as sodium, potassium and calcium.…”

Section: Introductionmentioning

confidence: 99%

Identification of Gly/NMDAR Antagonist from Chromolaena odorata’s Derived Phytoconstituents using Induced Fit Docking Approach

et al. 2019

Preprint

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The ionotropic activation of N-methyl-D-aspartic acid (NMDA) plays a significant role in different type of neurodegenerative disease, as it is a tetramer with two Glycine binding subunit and two glutamate subunits. NMDA receptor can be inhibited by either blocking of the glycine site or glutamate site. Previously reported inhibitors of NMDA receptor focus on the inhibition of the glutamate subunit, which was reported to be associated with side effects such as ataxia, memory deficits, neurotoxicity. Therefore, different compounds with antagonistic effect are been explored on Gly/NMDA site. Glide XP docking was employed in screening phyto-constituent of Chromolaena odorata against Gly/NMDA receptor for hit compounds with antagonistic properties. The hit compounds were further subjected to Induced fit docking (IFD) and lipinski rule of five. The final selection was based on Rigid XP docking score using co-crystallized ligand as threshold docking score, interaction with receptor site residues, and IFD score. Ferulic acid, caffeic acid and scutellarein recorded binding affinity of -8.752Kcal/mol, 10.004 Kcal/mol and -9.096 Kcal/mol respectively, which is higher than the binding affinity of co-crystallized ligand.Induced fit score obtained were -614.38, -614.03 and -616.31 for ferulic acid, caffeic acid and

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Dual Allosteric Effect in Glycine/NMDA Receptor Antagonism: A Comparative QSAR Approach

Cited by 8 publications

References 39 publications

In silico repurposing of antipsychotic drugs for Alzheimer’s disease

In silico repurposing of antipsychotic drugs for Alzheimer’s disease

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

Identification of Gly/NMDAR Antagonist from Chromolaena odorata’s Derived Phytoconstituents using Induced Fit Docking Approach

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