Novel insights into the dynamics behavior of glucagon-like peptide-1 receptor with its small molecule agonists

Girdhar, Khyati; Dehury, Budheswar; Singh, Mahender Kumar; Daniel, P. Vineeth; Choubey, Abhinav; Dogra, Surbhi; Kumar, Sunil; Mondal, Prosenjit

doi:10.1080/07391102.2018.1532818

Cited by 27 publications

(14 citation statements)

References 36 publications

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“…Gasteiger charges for the screening were obtained by using BIOVIA DSV. Protein and ligand preparations and molecular docking parameters were performed comparably according to our past report and contrasted with the small molecule GLP1R agonists ( 11 ). The docked conformations of each compound were ranked into clusters based on their binding energy, and the best conformations were visually analyzed.…”

Section: Methodsmentioning

confidence: 99%

“…GLP1R has an independently folded globular ectodomain (ECD) at their N-termini; any peptide agonist initiates receptor signaling by binding to the transmembrane domain and several amino acid residues on the ECD ( 10 ). Considering the importance of amino acid residues present on ECD in ligand binding, GLP1R agonist small molecules interaction and their dynamics with the extracellular domain of the GLP1R has been investigated ( 11 ). In this article, we designed a de novo series of a small molecule by considering the role of hydrophobic interactions (mainly π-π stacking) in ligand binding with the GLP1R extracellular domain.…”

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confidence: 99%

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Design, synthesis, and biological evaluation of a small molecule oral agonist of the glucagon-like-peptide-1 receptor

Girdhar

Thakur

Gaur

et al. 2022

Journal of Biological Chemistry

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Design, synthesis, and biological evaluation of a small molecule oral agonist of the glucagon-like-peptide-1 receptor

Girdhar

Thakur

Gaur

et al. 2022

Journal of Biological Chemistry

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“…To study the dynamic behavior, stability, and conformational flexibility RBD-ACE2- naltrexone complex, all-atoms MD simulations were performed as reported previously (Dehury et al., 2014 ; 2017 ; Girdhar et al., 2019 ). CHARMM36 force fields were used for topology building of protein in GROMACSv2019.4 package (Abraham et al., 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Naltrexone a potential therapeutic candidate for COVID-19

Choubey

Dehury

Kumar

et al. 2020

Journal of Biomolecular Structure and Dynamics

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the cause of Coronavirus Disease (COVID-19) that has resulted in a global pandemic. At the time of writing, approximately 16.06 million cases have been reported worldwide. Like other coronaviruses, SARS-CoV-2 relies on the surface Spike glycoprotein to access the host cells, mainly through the interaction of its Receptor Binding Domain (RBD) with the host receptor Angiotensin-Converting Enzyme2 (ACE2). SARS-CoV-2 infection induces a profound downstream pro-inflammatory cytokine storm. This release of the pro-inflammatory cytokines is underpinning lung tissue damage, respiratory failure, and eventually multiple organ failure in COVID-19 patients. The phosphorylation status of ERK1/2 is positively correlated with virus load and ERK1/2 inhibition suppressed viral replication and viral infectivity. Therefore, molecular entities able to interfere with binding of the SARS-CoV-2 Spike protein to ACE2, or damping hyperinflammatory cytokines storm, blocking ERK1/2 phosphorylation have a great potential to inhibit viral entry along with viral infectivity. Herein, we report that the FDA-approved non-peptide opioid antagonist drug, naltrexone suppresses high fat/LPS induced pro-inflammatory cytokine release both from macrophage cells and Adipose Tissue Macrophage. Moreover, Low Dose Naltrexone (LDN) also showed its activity as an ERK1/2 inhibitor. Notably, virtual docking and simulation data also suggest LDN may disrupt the interaction of ACE2 with RBD. LDN may be considered as a target as the treatment and (or) adjuvant therapy for coronavirus infection. Clinical toxicity measurements may not be required for LDN since naltrexone was previously tested and is an approved drug by the FDA.

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“…Dimensionality reduction of MD data with the use of PCA was also first used in the early 90s ( Ichiye and Karplus, 1991 ; Amadei et al, 1993 ) and since that time its application in MD output analysis has been constantly growing ( Das and Mukhopadhyay, 2007 ; Chiappori et al, 2010 ; Kim et al, 2010 ; Casoni et al, 2013 ; Ng et al, 2013 ; Novikov et al, 2013 ; Bhakat et al, 2014 ; Sittel et al, 2014 ; Ernst et al, 2015 ; Chaturvedi et al, 2017 ; Cossio-Pérez et al, 2017 ; Fakhar et al, 2017 ; Chen, 2018 ; Cholko et al, 2018 ; An et al, 2019 ; Barletta et al, 2019 ; Girdhar et al, 2019 ; Karnati and Wang, 2019 ; Lipiński et al, 2019 ; Martínez-Archundia et al, 2019 ; Wu et al, 2019 ; Magudeeswaran and Poomani, 2020 ; David et al, 2021 ; Majumder and Giri, 2021 ). Although PCA is the most popular approach applied to handle MD trajectories, other data dimensionality reduction methods are also used in the MD field.…”

Section: Clustering and Reduction Of Data Dimensionalitymentioning

confidence: 99%

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

Baltrukevich

Podlewska

2022

Front. Pharmacol.

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An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

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Novel insights into the dynamics behavior of glucagon-like peptide-1 receptor with its small molecule agonists

Cited by 27 publications

References 36 publications

Design, synthesis, and biological evaluation of a small molecule oral agonist of the glucagon-like-peptide-1 receptor

Design, synthesis, and biological evaluation of a small molecule oral agonist of the glucagon-like-peptide-1 receptor

Naltrexone a potential therapeutic candidate for COVID-19

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

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