ArticlePloS one2017
Structural features embedded in G protein-coupled receptor co-crystal structures are key to their success in virtual screening.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data.Journal of cheminformatics · 2022Article
- Insect RDL Receptor Models for Virtual Screening: Impact of the Template Conformational State in Pentameric Ligand-Gated Ion Channels.ACS omega · 2022Article
- G protein-coupled receptors: structure- and function-based drug discovery.Signal transduction and targeted therapy · 2021Review
- The Retinoid and Non-Retinoid Ligands of the Rod Visual G Protein-Coupled Receptor.International journal of molecular sciences · 2019Review
- Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design.Frontiers in pharmacology · 2018Review
- Improving virtual screening of G protein-coupled receptors via ligand-directed modeling.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structure based drug discovery on GPCRs harness atomic detail X-ray binding pockets and large libraries of potential drug lead candidates in virtual screening (VS) to identify novel lead candidates. Relatively small conformational differences between such binding pockets can be critical to the success of VS. Retrospective VS on GPCR/ligand co-crystal structures revealed stark differences in the ability of different structures to identify known ligands, despite being co-crystallized with the same ligand. When using the OpenEye toolkit and the ICM modeling package, we identify criteria associated with the predictive power of binding pockets in VS that consists of a combination of ligand/receptor interaction pattern and predicted ligand/receptor interaction strength. These findings can guide the selection and refinement of GPCR binding pockets for use in SBDD programs and may also provide a potential framework for evaluating the ability of computational GPCR binding pocket refinement tools in improving the predictive power of binding pockets.
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