Determination of the binding mode and interacting amino-acids for dibasic H3 receptor antagonists

Levoin, Nicolas; Labeeuw, Olivier; Krief, Stéphane; Calmels, Thierry; Poupardin-Olivier, Olivia; Berrebi-Bertrand, Isabelle; Lecomte, Jeanne‐Marie; Schwartz, Jean‐Charles; Capet, Marc

doi:10.1016/j.bmc.2013.05.035

Cited by 13 publications

(11 citation statements)

References 27 publications

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“…The results of docking indicate that the critical amino acids interacting with different parts of selected molecules are as follows: Glu 206 (TM5) in the interaction with basic moiety, Tyr 115 (TM3) and Tyr 374 (TM6) residues interact with linker, and Tyr 189 (TM5) and Phe 398 (TM7) forms the hydrophobic pocket with hydrophobic/aromatic moiety ( Figure S3.1). These findings are in close agreement with the findings published previously (Axe et al, 2006;Bajda et al, 2012;Harusawa et al, 2013;Kuder et al, 2016;Łażewska et al, 2016;Lepailleur et al, 2014;Levoin et al, 2013;Morini et al, 2006;Sheng et al, 2015;Wen et al, 2017).…”

Section: Discussionsupporting

confidence: 93%

“…Molecular docking of pitolisant as reference molecule was performed based on the identified key residues in the binding site resulting in docking comparable to those reported elsewhere (Kuder et al., ; Łażewska et al., ; Levoin et al., ; Morini et al., ). These amino acids might be the key residues of the binding site of H 3 R important for ligand binding as evidenced in several previously published reports (Axe, Bembenek, & Szalma, ; Kuder et al., ; Łażewska et al., ; Levoin et al., ; Morini et al., ; Schwartz, ). As structure‐based virtual screening method, the pharmacophore model of the receptor complex outlined above was generated by LigandScout and applied to online ZINCPharmer web server (Figure ).…”

Section: Discussionmentioning

confidence: 74%

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Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

et al. 2019

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Histamine H3 receptors (H3R), belonging to G‐protein coupled receptors (GPCR) class A superfamily, are responsible for modulating the release of histamine as well as of other neurotransmitters by a negative feedback mechanism mainly in the central nervous system (CNS). These receptors have gained increased attention as therapeutic target for several CNS related neurological diseases. In the current study, we aimed to identify novel H3R ligands using in silico virtual screening methods. To this end, a combination of ligand‐ and structure‐based approaches was utilized for screening of ZINC database on the homology model of human H3R. Structural similarity‐ and pharmacophore‐based approaches were employed to generate compound libraries. Various molecular modeling methodologies such as molecular docking and dynamics simulation along with different drug likeness filtering criteria were applied to select anti‐H3R ligands as promising candidate molecules based on different known parent lead compounds. In vitro binding assays of the selected molecules demonstrated three of them being active within the micromolar and submicromolar Ki range. The current integrated computational and experimental methods used in this work can provide new general insights for systematic hit identification for novel anti‐H3R agents from large compound libraries.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 74%

Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

et al. 2019

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“…The issue of the correct prediction of ligand binding mode in H3 receptor models has been also investigated by N. Levoin et al . based on two hH3R models[ 68 , 69 ]. First, where the human H3 receptor sequence (Q9Y5N1) was aligned with bovine rhodopsin (PDB: 1L9H) as a template.…”

Section: Resultsmentioning

confidence: 99%

Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification

2017

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The crucial role of G-protein coupled receptors and the significant achievements associated with a better understanding of the spatial structure of known receptors in this family encouraged us to undertake a study on the histamine H3 receptor, whose crystal structure is still unresolved. The latest literature data and availability of different software enabled us to build homology models of higher accuracy than previously published ones. The new models are expected to be closer to crystal structures; and therefore, they are much more helpful in the design of potential ligands. In this article, we describe the generation of homology models with the use of diverse tools and a hybrid assessment. Our study incorporates a hybrid assessment connecting knowledge-based scoring algorithms with a two-step ligand-based docking procedure. Knowledge-based scoring employs probability theory for global energy minimum determination based on information about native amino acid conformation from a dataset of experimentally determined protein structures. For a two-step docking procedure two programs were applied: GOLD was used in the first step and Glide in the second. Hybrid approaches offer advantages by combining various theoretical methods in one modeling algorithm. The biggest advantage of hybrid methods is their intrinsic ability to self-update and self-refine when additional structural data are acquired. Moreover, the diversity of computational methods and structural data used in hybrid approaches for structure prediction limit inaccuracies resulting from theoretical approximations or fuzziness of experimental data. The results of docking to the new H3 receptor model allowed us to analyze ligand—receptor interactions for reference compounds.

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“…Levoin and coworkers analyzed the binding mode of dibasic H3 ligands using a H1R based H3R model [40]. They proposed a ligand orientation perpendicular to the membrane plane, bridging Glu206(5.46) to acidic amino acids of the extracellular loops, such as Glu175(4.51) and Glu191(5.31).…”

Section: H3 Receptormentioning

confidence: 99%

Structure-based discovery and binding site analysis of histamine receptor ligands

Kingsford-Adaboh

Keserü

2016

Expert Opinion on Drug Discovery

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Introduction: Application of structure-based drug discovery in histamine receptor projects was previously hampered by the lack of experimental structures. The publication of the first X-ray structure of the histamine H1 receptor has been followed by several successful virtual screens and binding site analysis studies of H1-antihistamines. This structure together with several other recently solved aminergic G-protein coupled receptors (GPCRs) enabled the development of more realistic homology models for H2, H3 and H4 receptors. Areas covered: In this paper, we review the development of histamine receptors models and their application on drug discovery. Expert opinion: In our opinion, the application of atomistic histamine receptor models played a significant role in understanding key ligand-receptor interactions as well as in the discovery of novel chemical starting points. The recently solved H1 receptor structure is a major milestone in structure-based drug discovery, however our analysis also demonstrate that for building H3 and H4 receptor homology models other GPCRs may be more suitable as a template. For these receptors we envisage that the development of higher quality homology models will significantly contribute to the discovery and optimization of novel H3 and H4 ligands.

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Determination of the binding mode and interacting amino-acids for dibasic H3 receptor antagonists

Cited by 13 publications

References 27 publications

Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification

Structure-based discovery and binding site analysis of histamine receptor ligands

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Determination of the binding mode and interacting amino-acids for dibasic H3 receptor antagonists

Cited by 13 publications

References 27 publications

Histamine H3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Histamine H3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification

Structure-based discovery and binding site analysis of histamine receptor ligands

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Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Histamine H₃ receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects