Peeking at G-protein-coupled Receptors Through the Molecular Dynamics Keyhole

Deganutti, Giuseppe; Moro, Stefano; Reynolds, Christopher A.

doi:10.4155/fmc-2018-0393

Cited by 12 publications

(10 citation statements)

References 171 publications

(179 reference statements)

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“…We have also shown that extending the simulations' length from 100 up to 500 ns does not significantly enhance the conformational sampling or provide better understanding of the transitions between the loop's states. In contrast, we observed AnEH's conformational changes related to the N-terminal region's movements when we extended the simulations, in line with the study of [48]. We therefore recommend for MD simulations using more repetitions of sufficient length, rather than extending the simulations towards microsecond lengths.…”

Section: Discussionsupporting

confidence: 74%

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Mitusińska

Skalski

Góra

2020

IJMS

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Loops are the most variable and unorganized elements of the secondary structure of proteins. Their ability to shift their shape can play a role in the binding of small ligands, enzymatic catalysis, or protein–protein interactions. Due to the loop flexibility, the positions of their residues in solved structures show the largest B-factors, or in a worst-case scenario can be unknown. Based on the loops’ movements’ timeline, they can be divided into slow (static) and fast (flexible). Although most of the loops that are missing in experimental structures belong to the flexible loops group, the computational tools for loop reconstruction use a set of static loop conformations to predict the missing part of the structure and evaluate the model. We believe that these two loop types can adopt different conformations and that using scoring functions appropriate for static loops is not sufficient for flexible loops. We showed that common model evaluation methods, are insufficient in the case of flexible solvent-exposed loops. Instead, we recommend using the potential energy to evaluate such loop models. We provide a novel model selection method based on a set of geometrical parameters to distinguish between flexible and static loops without the use of molecular dynamics simulations. We have also pointed out the importance of water network and interactions with the solvent for the flexible loop modeling.

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Section: Discussionsupporting

confidence: 74%

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Mitusińska

Skalski

Góra

2020

IJMS

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“…were observed for TM7, ECL2 and ECL3, and ICL2 and ICL3 when compared to the unbound structure. The concept of stabilization of an inactive conformation of the target GPCR upon antagonist binding was postulated for several receptors and supported by mutagenesis experiments, biophysical studies and MD simulations [58,[66][67][68]. This had also been observed for the P2Y1 receptor which belongs to the same δ-branch of the class A family of GPCRs.…”

Section: Simulation Study Of Antagonistsmentioning

confidence: 82%

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

et al. 2020

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GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆9-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.

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“…MD simulations and mutagenesis experiments are frequently combined to deliver structural insights on endpoint protein-ligand complexes. To the best of our knowledge [67][68][69] this is the first that the whole process of formation/dissociation of several GPCR ligands is reconstructed with a combined in silico and in vitro approach. Our results pave the way to the rationalization of structure-kinetic relationship (SKR) for A 1 R, with potential repercussion on the rational design of long-awaited clinical agents.…”

Section: Resultsmentioning

confidence: 99%

Deciphering the Agonist Binding Mechanism to the Adenosine A1 Receptor

Deganutti

Barkan

Preti

et al. 2020

Preprint

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Despite being amongst the most characterized G protein-coupled receptors (GPCRs), adenosine receptors (ARs) have always been a difficult target in drug design. To date, no agonist other than the natural effector and the diagnostic regadenoson has been approved for human use. Recently, the structure of the adenosine A1 receptor (A1R) was determined in the active, Gi protein complexed state; this has important repercussions for structure-based drug design. Here, we employed supervised molecular dynamics simulations and mutagenesis experiments to extend the structural knowledge of the binding of selective agonists to A1R. Our results identify new residues involved in the association and dissociation pathway, suggest the binding mode of N6-cyclopentyladenosine (CPA) related ligands, and highlight the dramatic effect that chemical modifications can have on the overall binding mechanism.

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Peeking at G-protein-coupled Receptors Through the Molecular Dynamics Keyhole

Cited by 12 publications

References 171 publications

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

Deciphering the Agonist Binding Mechanism to the Adenosine A1 Receptor

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