A Strategy Combining Differential Low‐Throughput Screening and Virtual Screening (DLS‐VS) Accelerating the Discovery of new Modulators for the Orphan GPR34 Receptor. Issue 2 (11th February 2013)

• Record Type: Journal Article
• Authors: Diaz, Constantino; Labit‐Le Bouteiller, Christine; Yvon, Stéphane; Cambon‐Kernëis, Aimée; Roasio, Annette; Jamme, Marie‐Françoise; Aries, Amélie; Feuillerat, Claude; Perret, Eric; Guette, Fréderique; Dieu, Pierre; Miloux, Brigitte; Albène, Danielle; Hasel, Nathalie; Kaghad, Mourad; Ferran, Edgardo; Lupker, Jan; Ferrara, Pascual
• Is Part Of: Molecular informatics. Volume 32:Issue 2(2013:Feb.)
• Page Start: 213
• Page End: 229
• Publication Date: 2013-02-11
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201200047
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>The DLS‐VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low‐throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators specific for the considered receptor among a set of receptors, through the screening of a small library with diverse chemical compounds. Then, an active molecular model of the receptor is built by homology to a validated template, and it is progressively refined by rotamers modification for key side‐chains, by VS of the already screened library, and by iterative selection of the model generating the best enrichment. The refined active model is finally used for the VS of a large chemical library and the selection of a small set of compounds for experimental testing. Applied to the orphan receptor GPR34, the DLS‐VS strategy combined the experimental screening of 20 000 compounds and the virtual screening of 1 250 000 compounds. It identified one agonist and eight inverse agonists, showing a high chemical diversity. We describe the method. The strategy can be applied to other GPCRs.</p> </abstract>