Insights into binding modes of adenosine A2B antagonists with ligand-based and receptor-based methods

Cheng, Feixiong; Xu, Zhejun; Liu, Guixia; Tang, Yun

doi:10.1016/j.ejmech.2010.04.039

Cited by 29 publications

(16 citation statements)

References 51 publications

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“…However, as the xanthine ligands are docked into a published adenosine A 2A crystal structure (PDB: 3EML) in which Tyr271 is in the gauche negative conformation the binding mode suggested here is not possible. 30 This is because the top of the binding site is partially occluded in the position occupied by the substituted phenyl group. However, the binding pose of xanthines proposed by Cheng et al in A 2B is very similar to the results presented here because Y271 (A 2A ) is actually N273 (A 2B ), and thus, there is no residue obstructing this portion of the binding site.…”

Section: Resultsmentioning

confidence: 99%

Biophysical Mapping of the Adenosine A_2A Receptor

et al. 2011

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A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophysical Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, additional single mutations are then added at positions that could be involved in small molecule interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small molecule ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands versus each active site mutation is then generated, providing specific affinity and kinetic information (KD, kon, and koff) of receptor–ligand interactions. This data set, in combination with molecular modeling and docking, is used to map the small molecule binding site for each class of compounds. Taken together, the many constraints provided by these data identify key protein–ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophysical mapping of the adenosine A2A receptor are presented.

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Section: Resultsmentioning

confidence: 99%

Biophysical Mapping of the Adenosine A_2A Receptor

et al. 2011

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“…Pharmacophore modeling is another widely used ligand-based method for drug discovery. It can be used to discover novel ligands with diverse chemical types [17][18][19]. Many pharmacophore models have been built for GPCR ligands [20,21], but none was reported for H 2 R agonists.…”

Section: Introductionmentioning

confidence: 99%

Computational investigation of interactions between human H2 receptor and its agonists

Sun

et al. 2011

Journal of Molecular Graphics and Modelling

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“…However, a large number of subtype selective ligands is also available, which might provide additional information for subtype selectivity. Pharmacophore modeling is a well‐established ligand‐based method in rational drug design 12–14. A pharmacophore model is a collection of chemical features in three‐dimensional (3D) space to mark the key features of ligand‐receptor interactions.…”

Section: Introductionmentioning

confidence: 99%

Computational Insights into Ligand Selectivity of Estrogen Receptors from Pharmacophore Modeling

Fang

Shen

Cheng

et al. 2011

Molecular Informatics

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Estrogen receptors (ERs) belong to the nuclear receptor superfamily, which play crucial roles in the human body. To date, two subtypes, ERα and ERβ, have been identified. The function and expression of both subtypes are various, stimulating the interest for discovering subtype selective ligands. However, ERα and ERβ are highly homologous. They share 56 % sequence identity in the ligand binding domain and only two pairs of residues differ in the ligand binding pocket. In this study, pharmacophore models were built for both subtypes based on 23 ERα and 24 ERβ selective ligands, respectively. It was found from these pharmacophore models that hydrophobic and hydrogen bonding interactions were essential for the subtype selectivity. The hydrogen bond donor feature in the phenol part was a favorable contribution to ERβ selectivity, whereas the one in the benzopyran part of the ligand was important for ERα selectivity. Furthermore, simulated virtual screening was performed with both subtype specific pharmacophore models. The results confirmed the reliability and discrimination ability of both models. These findings could not only be helpful for the understanding of a possible mechanism underlying ligand selectivity of ERs, but could also pave a new avenue for the discovery of novel selective ER ligands.

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Insights into binding modes of adenosine A2B antagonists with ligand-based and receptor-based methods

Cited by 29 publications

References 51 publications

Biophysical Mapping of the Adenosine A_2A Receptor

Biophysical Mapping of the Adenosine A_2A Receptor

Computational investigation of interactions between human H2 receptor and its agonists

Computational Insights into Ligand Selectivity of Estrogen Receptors from Pharmacophore Modeling

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Insights into binding modes of adenosine A2B antagonists with ligand-based and receptor-based methods

Cited by 29 publications

References 51 publications

Biophysical Mapping of the Adenosine A2A Receptor

Biophysical Mapping of the Adenosine A2A Receptor

Computational investigation of interactions between human H2 receptor and its agonists

Computational Insights into Ligand Selectivity of Estrogen Receptors from Pharmacophore Modeling

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Product

Resources

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Biophysical Mapping of the Adenosine A_2A Receptor

Biophysical Mapping of the Adenosine A_2A Receptor