&lt;strong&gt;Creating a valid &lt;em&gt;in silico&lt;/em&gt; Dopamine D2-receptor model for small molecular docking studies&lt;/strong&gt;

Bueschbell, Beatriz; Preto, António; Barreto, Carlos; Schiedel, Anke C.; Moreira, Irina S.

doi:10.3390/mol2net-03-05088

Cited by 2 publications

(12 citation statements)

References 32 publications

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“…However, Sukalovic et al, who used D3R crystal structure as template for D2R modeling, and then docked their own synthesized dopaminergic arylpiperazines, attained binding energies around -10 kcal/mol, in line with our results [60]. The binding pocket was defined according to previous studies from literature [41]. Foremost, 3.32Asp, a serine microdomain (5.42Ser, 5.43Ser, 5.46Ser) and an aromatic domain in TM6 (6.48Trp, 6.51Phe, 6.52Phe, 6.55His) are believed to be crucial for dopaminergic binding and receptor activation [38].…”

Section: Molecular Docking and Definition Of The Binding Pocketsupporting

confidence: 83%

“…In this work, we used the comprehensive review of Floresca and Schnetz (2004) [38], highly used [39][40][41], as a base for the definition of the binding pocket of all dopamine receptors. Furthermore, by applying Ballesteros & Weinstein numbering (B&W) [42] the position of considered important residues was more easily comparable between all receptors.…”

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

“…Furthermore, by applying Ballesteros & Weinstein numbering (B&W) [42] the position of considered important residues was more easily comparable between all receptors. Mutagenesis studies have shown that for dopamine binding, the endogenous agonist of the DR, a negatively charged aspartate (3.32Asp) is believed to form a ionic bond interaction with the protonable amine of dopamine [2,41,43]. Moreover, it was shown that this effect was crucial for ligand binding and that this amino acid was not only conserved among the DR, but also in all biogenic amine GPCRs [44,45].…”

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

“…A further microdomain, the aromatic microdomain, consisting of 6.48Trp, 6.51Phe, 6.52Phe and 6.55His/Asn has been reported to trigger the activation of the dopamine receptor. All amino-acids in this microdomain share the same hydrophobic face in the water-assessable binding-site crevice, indicating that any reorientation of these residues by binding to a ligand would cause steric clashes and therefore would force the residues to reorient themselves in a domino-like fashion, which lastly leads to the so-called "rotamer toggle switch" [38,41,44,49]. In addition, 6.48Trp was reported together with 6.55His to stabilize the position of the ligand in the binding pocket via π-π-stacking [38,49].…”

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

“…There are several approaches to validate homology models such as built-in metrics of open-source [43] and licensed softwares [62]. In a preliminary study we experienced [41] that the combination of different independent metrics provided adequate models suitable for molecular docking. For instance, the combination of MODELLER's metrics [31], ProSA-web [63,64] and ProQ [65] revealed to be promising and again straightforward.…”

Section: Model Metricsmentioning

confidence: 99%

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A Complete Assessment of Dopamine Receptor-Ligand Interactions through Computational Methods

Bueschbell¹,

Barreto²,

Preto³

et al. 2019

Preprint

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Background: Selectively targeting dopamine receptors has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases evolving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated.Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2like receptors). Fifteen structurally diverse ligands were docked to these models. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for DR sub-type specificity. Results:We showed that the number of conformations for a receptor:ligand complex was associated to unspecific interactions > 2.5 Å and hydrophobic contacts, while the decoys binding energy was influenced by specific electrostatic interactions. Known residues such as 3.32Asp, the serine microdomain and the aromatic microdomain were found interacting in a variety of modes (HB, SB, πstacking). Purposed TM2-TM3-TM7 microdomain was found to form a hydrophobic network involving Orthosteric Binding Pocket (OBP) and Secondary Binding Pocket (SBP). T-stacking interactions revealed as especially relevant for some large ligands such as apomorphine, risperidone or aripiprazole.Conclusions: This in silico approach was successful in showing known receptorligand interactions as well as in determining unique combinations of interactions, key for the design of more specific ligands.The strive for finding new and effective therapeutics led to a growing interest in the use of Computer Aided Drug Design (CADD). Originally developed for High-Throughput Screening (HTS) of compound libraries, the use of CADD nowadays plays an important role in drug discovery [24]. Modeling three-dimensional (3D) target proteins help to visualize, analyse and optimize known ligands and discover new lead compounds [25]. The CADD pipeline can be classified in two general categories: structure-based and ligand-based, dependent on the available information about the topic of investigation [25]. A structure-based CADD is used when the target, e.g. a receptor, is known and so compound libraries can be screened to find suitable structures for the target. Usually protein-ligand docking studies are performed or ligands are designed de novo and are then used for compound library screening to test possible lead structures experimentally. Vice-versa, a ligand-based CAAD procedure is used when ligand structure information is provided to create pharmacophore models and to perform virtual screening [24]. All in all, CADD faces the challenges of identifying novel targets and their ligands, for example to treat common and rare diseases [26]. AimModeling G protein-coupled receptors (GPCRs) remains challenging due to the complex structure of these membrane proteins and the lack of structural information about the desired receptor to target, however CADD m...

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Section: Molecular Docking and Definition Of The Binding Pocketsupporting

confidence: 83%

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

Section: Ligand Binding To Dopamine Receptorsmentioning

confidence: 99%

Section: Model Metricsmentioning

confidence: 99%

See 3 more Smart Citations

A Complete Assessment of Dopamine Receptor-Ligand Interactions through Computational Methods

Bueschbell¹,

Barreto²,

Preto³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods

Bueschbell

Barreto²,

Preto³

et al. 2019

Molecules

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Background: Selectively targeting dopamine receptors (DRs) has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases involving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like). Fifteen structurally diverse ligands were docked. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for selective binding to DR subtypes. Results: Residues of the aromatic microdomain were shown to be responsible for the majority of ligand interactions established to all DRs. Hydrophobic contacts involved a huge network of conserved and non-conserved residues between three transmembrane domains (TMs), TM2-TM3-TM7. Hydrogen bonds were mostly mediated by the serine microdomain. TM1 and TM2 residues were main contributors for the coupling of large ligands. Some amino acid groups form electrostatic interactions of particular importance for D1R-like selective ligands binding. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, which will support mutagenesis studies to improve the design of subtype-specific ligands.

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<strong>Creating a valid <em>in silico</em> Dopamine D2-receptor model for small molecular docking studies</strong>

Cited by 2 publications

References 32 publications

A Complete Assessment of Dopamine Receptor-Ligand Interactions through Computational Methods

A Complete Assessment of Dopamine Receptor-Ligand Interactions through Computational Methods

A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods

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