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New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

ABSTRACT: Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A1 (A1AR) has been recently solved,providing important structural insights. In the present work, we rationalized the selectivity profile of two selective A1AR and A2AAR antagonists, investigating their recognition trajectories obtained by Supervised Molecular Dynamics from an unbound state and monitoring the role of the water molecules in the binding site.

SUBMITTER: Bolcato G

PROVIDER: S-EPMC7278174 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

Bolcato Giovanni G Bissaro Maicol M Deganutti Giuseppe G Sturlese Mattia M Moro Stefano S

Biomolecules 20200507 5

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A<sub>1</sub> (A<sub>1</sub>AR) has been recently solved,providing i ...[more]

PMID: 32392873

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Project description:Quinine's ability to bind DNA and potentially inhibit transcription and translation has been examined as a mode of action for its antimalarial activity. UV absorption and fluorescence-based studies have lacked the chemical specificity to develop an unambiguous molecular-level picture of the binding interaction. To address this, we use Raman spectroscopy and molecular dynamics (MD) to investigate quinine-DNA interactions. We demonstrate that quinine's strongest Raman band in the fingerprint region, which derives from a symmetric stretching mode of the quinoline ring, is highly sensitive to the local chemical environment and pH. The frequency shifts observed for this mode in solvents of varying polarity can be explained in terms of the Stark effect using a simple Onsager solvation model, indicating that the vibration reports on the local electrostatic environment. However, specific chemical interactions between the quinoline ring and its environment, such as hydrogen bonding and ?-stacking, perturb the frequency of this mode in a more complicated but predictable manner. We use this vibration as a spectroscopic probe to investigate the binding interaction between quinine and DNA. We find that, when the quinoline ring is protonated, quinine weakly intercalates into DNA by forming ?-stacking interactions with the base pairs. The Raman spectra indicate that quinine can intercalate into DNA with a ratio reaching up to roughly one molecule per 25 base pairs. Our results are confirmed by MD simulations, which also show that the quinoline ring adopts a t-shaped ?-stacking geometry with the DNA base pairs, whereas the quinuclidine head group weakly interacts with the phosphate backbone in the minor groove. We expect that the spectral correlations determined here will enable future studies to probe quinine's antimalarial activities, such as disrupting hemozoin biocrystallization, which is hypothesized to be, among other things, one of its primary modes of action against Plasmodium parasites.

Dataset Information

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

Publications

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

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