Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

Wei, Yu; Wang, Mukuo; Li, Yang; Hong, Zhangyong; Li, Dongmei; Lin, Jianping

doi:10.1016/j.ejmech.2019.111936

Cited by 19 publications

(22 citation statements)

References 66 publications

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“…237 Through VS of 3.1 million molecules against M2 and M3, Kruse et al identified a partial M3 agonist without measurable M2 agonism, capable of stimulating insulin release from a mouse β-cell line. 238 Wei et al reported a multistage VS of the ChemDiv library (1,492,362 compounds) toward A 1 AR and discovered four novel antagonists with good affinity and selectively (>100-fold) over A 2A R. 239 Virtual screening of ultralarge libraries [224][225][226] Novel scaffolds and chemotypes, higher chances of finding potent ligands…”

Section: Subtype Selectivitymentioning

confidence: 99%

G protein-coupled receptors: structure- and function-based drug discovery

Yang

Zhou

Labroska

et al. 2021

Sig Transduct Target Ther

338

280

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As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure–function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

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Section: Subtype Selectivitymentioning

confidence: 99%

G protein-coupled receptors: structure- and function-based drug discovery

Yang

Zhou

Labroska

et al. 2021

Sig Transduct Target Ther

338

280

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“…Our previous study showed that a multistage approach sequentially integrating machine learning classification models and pharmacophore and molecular docking methods is efficient in identifying bioactive compounds from chemical databases [52]. In this study, the performance of the hierarchical multistage virtual screening approach was evaluated through a validation set containing 433 bioactive dual A 1 /A 2A AR antagonists (40 nM < K i <600 nM) and 11605 decoys (S2 Table).…”

Section: Virtual Screeningmentioning

confidence: 99%

“…In this work, 19 tested compounds were purchased from J&K Scientific Ltd. (Shanghai, China). Functional assays were performed by Pharmaron (Beijing) as previously described [52] by evaluating AR-mediated cAMP production using CHO-A 1 cells and HEK293-A 2A cells stably expressing A 1 AR or A 2A AR, respectively. The CHO-A 1 cells and HEK293-A 2A cells were cultured in growth medium (Ham's F12K (A 1 AR)/DMEM (A 2A AR) + 10% FBS + 1 � Ps + 400 μg/ml G418) at 37˚C and 5% CO 2 , collected by centrifugation and resuspended in Hank's balanced salt solution (HBSS), 0.1% bovine serum albumin (BSA), 20 mM N-(2-hydroxyethyl)-piperazine-N0-ethanesulfonic acid (HEPES) and 100 nM 3-isobutyl-1-methylxanthine (IBMX).…”

Section: Functional Assaymentioning

confidence: 99%

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking

et al. 2021

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Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson’s disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding assay); of these hits, two compounds (C8 and C9) with 1,2,4-triazole scaffolds possessing the most potent binding affinity and antagonistic activity for A1/A2A ARs at the nanomolar level (pKi of 7.16–7.49 and pIC50 of 6.31–6.78) were identified. Further molecular dynamics (MD) simulations suggested similarly strong binding interactions of the complexes between the A1/A2A ARs and two compounds (C8 and C9). Notably, the 1,2,4-triazole derivatives (compounds C8 and C9) were identified as the most potent dual A1/A2A AR antagonists in our study and could serve as a basis for further development. The effective multistage screening approach developed in this study can be utilized to identify potent ligands for other drug targets.

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“…Machine learning models could be beneficial for lead optimization and chemical compound prioritization when using computer-aided drug design ( Lavecchia, 2015 ). Statistical learning algorithms, namely, Naïve Bayesian ( Murakami and Mizuguchi, 2010 ; Fang et al, 2013 ) random forests (RFs) ( Jayaraj et al, 2016 ; Wei et al, 2016 ; Li et al, 2019a ; Wei et al, 2020 ), support vector machines (SVMs) ( Han et al, 2008 ; Mahé and Vert, 2009 ; Fang et al, 2013 ; Jayaraj and Jain, 2019 ; Wei et al, 2019 ), decision stump ( Nand et al, 2020 ), artificial neural networks (ANNs) ( Lobanov, 2004 ; Li et al, 2019b ), and k nearest neighbors (kNNs) ( Mahé and Vert, 2009 ), have been used to build models and effectively employed in virtual screening, prediction of protein–protein interactions, ADMET prediction, and pharmacokinetic studies with substantial outputs. Kadioglu and co-workers applied a workflow of combined virtual drug screening, molecular docking, and supervised machine learning algorithms to identify novel drug candidates against COVID-19 ( Kadioglu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting New Anti-Norovirus Inhibitor With the Help of Machine Learning Algorithms and Molecular Dynamics Simulation–Based Model

2021

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Hepatitis C virus (HCV) inhibitors are essential in the treatment of human norovirus (HuNoV). This study aimed to map out HCV NS5B RNA-dependent RNA polymerase inhibitors that could potentially be responsible for the inhibitory activity of HuNoV RdRp. It is necessary to develop robust machine learning and in silico methods to predict HuNoV RdRp compounds. In this study, Naïve Bayesian and random forest models were built to categorize norovirus RdRp inhibitors from the non-inhibitors using their molecular descriptors and PubChem fingerprints. The best model observed had accuracy, specificity, and sensitivity values of 98.40%, 97.62%, and 97.62%, respectively. Meanwhile, an external test set was used to validate model performance before applicability to the screened HCV compounds database. As a result, 775 compounds were predicted as NoV RdRp inhibitors. The pharmacokinetics calculations were used to filter out the inhibitors that lack drug-likeness properties. Molecular docking and molecular dynamics simulation investigated the inhibitors’ binding modes and residues critical for the HuNoV RdRp receptor. The most active compound, CHEMBL167790, closely binds to the binding pocket of the RdRp enzyme and depicted stable binding with RMSD 0.8–3.2 Å, and the RMSF profile peak was between 1.0–4.0 Å, and the conformational fluctuations were at 450–460 residues. Moreover, the dynamic residue cross-correlation plot also showed the pairwise correlation between the binding residues 300–510 of the HuNoV RdRp receptor and CHEMBL167790. The principal component analysis depicted the enhanced movement of protein atoms. Moreover, additional residues such as Glu510 and Asn505 interacted with CHEMBL167790 via water bridge and established H-bond interactions after the simulation. http://zinc15.docking.org/substances/ZINC000013589565.

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Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

Cited by 19 publications

References 66 publications

G protein-coupled receptors: structure- and function-based drug discovery

G protein-coupled receptors: structure- and function-based drug discovery

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking

Predicting New Anti-Norovirus Inhibitor With the Help of Machine Learning Algorithms and Molecular Dynamics Simulation–Based Model

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