Dataset Information

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches.

ABSTRACT: Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC50 = 7.54407; ZINC04257548 pEC50 = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A2AAR.

SUBMITTER: Santos KLBD

PROVIDER: S-EPMC7179438 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches.

Santos Kelton L B Dos KLBD Cruz Jorddy N JN Silva Luciane B LB Ramos Ryan S RS Neto Moysés F A MFA Lobato Cleison C CC Ota Sirlene S B SSB Leite Franco H A FHA Borges Rosivaldo S RS Silva Carlos H T P da CHTPD Campos Joaquín M JM Santos Cleydson B R CBR

Molecules (Basel, Switzerland) 20200310 5

Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AA ...[more]

PMID: 32164183

Similar Datasets

Project description:BACKGROUND:The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. RESULTS:The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. CONCLUSIONS:We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Project description:Adenosine is generated in increased concentrations at sites of injury/hypoxia and mediates a variety of physiological and pharmacological effects via G protein-coupled receptors (A(1), A(2A), A(2B), and A(3)). Because all adenosine receptors are expressed on osteoclasts, we determined the role of A(2A) receptor in the regulation of osteoclast differentiation. Differentiation and bone resorption were studied as the macrophage colony-stimulating factor-1-receptor activator of NF-κB ligand formation of multinucleated tartrate-resistant acid phosphatase (TRAP)-positive cells from primary murine bone marrow-derived precursors. A(2A) receptor and osteoclast marker expression levels were studied by RT-PCR. Cytokine secretion was assayed by enzyme-linked immunosorbent assay. In vivo examination of A(2A) knockout (KO)/control bones was determined by TRAP staining, micro-computed tomography, and electron microscopy. The A(2A) receptor agonist, CGS21680, inhibited osteoclast differentiation and function (half maximal inhibitory concentration, 50 nmol/L), increased the percentage of immature osteoclast precursors, and decreased IL-1β and tumor necrosis factor-α secretion, an effect that was reversed by the A(2A) antagonist, ZM241385. Cathepsin K and osteopontin mRNA expression increased in control and ZM241385-pretreated osteoclasts, and this was blocked by CGS21680. Micro-computed tomography of A(2A)KO mouse femurs showed a significantly decreased bone volume/trabecular bone volume ratio, decreased trabecular number, and increased trabecular space. A(2A)KO femurs showed an increased TRAP-positive osteoclast. Electron microscopy in A(2A)KO femurs showed marked osteoclast membrane folding and increased bone resorption. Thus, adenosine, acting via the A(2A) receptor, inhibits macrophage colony-stimulating factor-1-receptor activator of NF-κB ligand-stimulated osteoclast differentiation and may regulate bone turnover under conditions in which adenosine levels are elevated.

Dataset Information

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches.

Publications

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure