Molecular dynamic simulation of mGluR5 amino terminal domain: essential dynamics analysis captures the agonist or antagonist behaviour of ligands

Casoni, Alessandro; Clerici, Francesca; Contini, Alessandro

doi:10.1016/j.jmgm.2013.02.002

Cited by 7 publications

(9 citation statements)

References 43 publications

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“…Aiming to carry out our research on Rac1 inhibitors and considering our interest in computational methods for drug discovery, ,,− we decided to attempt a de novo design of a chemically diverse lead compound, starting from the previously described model of the complex between Rac1 and 1 . The 2-amino-3-(phenylsulfanyl)norbornane-2-carboxylate scaffold was then selected for the preparation of new Rac1–Tiam1 PPI inhibitors having common pharmacophoric features with the known compounds but being at the same time structurally unrelated (Figure ).…”

Section: Introductionmentioning

confidence: 99%

2-Amino-3-(phenylsulfanyl)norbornane-2-carboxylate: An Appealing Scaffold for the Design of Rac1–Tiam1 Protein–Protein Interaction Inhibitors

et al. 2014

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The use of the 2-amino-3-(phenylsulfanyl)norbornane-2-carboxylate scaffold has been exploited for the de novo design of potent Rac1 inhibitors acting as modulators of the protein-protein interaction between Rac1 and Tiam1. A series of compounds differing in regio- and stereochemistry has been prepared by way of a multistep synthesis based on cycloaddition reactions and Pd chemistry. Pharmacological analyses showed that all the prepared compounds were active and selective for Rac1, and the most effective compound 13 was capable of inhibiting smooth muscle cell migration. The synthesis of this derivative was successfully scaled up to 1 g.

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Section: Introductionmentioning

confidence: 99%

2-Amino-3-(phenylsulfanyl)norbornane-2-carboxylate: An Appealing Scaffold for the Design of Rac1–Tiam1 Protein–Protein Interaction Inhibitors

et al. 2014

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“…An inter-LBR twist has previously been observed with the iGluR 21, 58 and other sub-families of the mGluRs. 59–61 …”

Section: Resultsmentioning

confidence: 99%

“…While we use the dimeric LBR system to retain the inter-LBR communication, since the aim of this work is to quantify the effect of a ligand on the conformational opening/closing of an LBR, we do not further discuss the inter-LBR conformational changes, but refer to these earlier studies that discuss this in more detail. 58, 59, 61 kcal/mol. Glutamate (upper left), DHPG (middle left), Z-CBQA (middle right) and conformation (1) of C3H2MPG keeps the LBR closed and active, while conformation (2) of C3H2MPG (lower right) deactivates the pocket, by driving the conformational minimum to be similar to an empty, open pocket (upper right).…”

Section: Resultsmentioning

confidence: 99%

Estimation of Ligand Efficacies of Metabotropic Glutamate Receptors from Conformational Forces Obtained from Molecular Dynamics Simulations

Lakkaraju

Xue

Faden

et al. 2013

J. Chem. Inf. Model.

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Group 1 metabotropic glutamate receptors (mGluR) are G-protein coupled receptors with a large bilobate extracellular ligand binding region (LBR) that resembles a Venus fly trap. Closing of this LBR in the presence of a ligand is associated with the activation of the receptor. From conformational sampling of the LBR-ligand complexes using all-atom molecular dynamics (MD) simulations, we characterized the conformational minima related to the hinge like motion associated with the LBR closing/opening in the presence of known agonists and antagonists. By applying a harmonic restraint on the LBR, we also determined the conformational forces generated by the different ligands. The change in the location of the minima and the conformational forces were used to quantify the efficacies of the ligands. This analysis shows that efficacies can be estimated from the forces of a single conformation of the receptor, indicating the potential of MD simulations as an efficient and useful technique to quantify efficacies thereby facilitating the rational design of mGluR agonists and antagonists.

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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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Molecular dynamic simulation of mGluR5 amino terminal domain: essential dynamics analysis captures the agonist or antagonist behaviour of ligands

Cited by 7 publications

References 43 publications

2-Amino-3-(phenylsulfanyl)norbornane-2-carboxylate: An Appealing Scaffold for the Design of Rac1–Tiam1 Protein–Protein Interaction Inhibitors

2-Amino-3-(phenylsulfanyl)norbornane-2-carboxylate: An Appealing Scaffold for the Design of Rac1–Tiam1 Protein–Protein Interaction Inhibitors

Estimation of Ligand Efficacies of Metabotropic Glutamate Receptors from Conformational Forces Obtained from Molecular Dynamics Simulations

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

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