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Design of high-content and focused libraries to improve the development of new active compounds in the framework of the rational drug discovery Nicolas Foata, Esther Kellenberger, Mireille Krier, Pascal Muller, Claire Schalon, Jean-Sébastien Surgand, Guillaume Bret and Didier Rognan CNRS UMR 7175 – LC1 Bioinformatics of the drug F-67400 ILLKIRCH-GRAFFENSTADEN, FRANCE [email protected]: +33 (0)3-90-24-42-24 Introduction Nowadays, new communication and information technologies give access to an important quantity of specific data. The second one named "screening-Protein Data Bank" (sc-PDB) is a collection of 6415 druggable binding sites from However, in chemoinformatics, such data are often too generic, focused on a single application field and not suited to a proteins whose x-ray structure has been deposited in the Protein Data Bank (PDB). The last one, “human G-Protein precise problem. Consequently, we generated and conceived several databases allowing the crossing of miscellaneous Coupled Receptors & ligands” (hGPCR-lig), is a collection of human GPCR (369) and their ligands (17908), also information. The first one called “Bioinfo”, is a library of 1.8 million commercially available drug-like compounds that can be classified according to the diversity of receptor binding sites and their ligands, respectively. use in the framework of the in silico screening. Filtration of the entries Databases hGPCR - lig Bioinfo 1. Goal - Objective 1. Goal - Objective Bank of 3-D human G-Protein Coupled Receptor models and their known ligands. Library setting up of commercially available drug-like compounds. 2. Data presentation 1 .. 23 Suppliers 2. Data presentation 2-D catalogues Receptors 369 human GPCRs Steps Compilation – Cleaning (PipeLine Pilot) [2] Scaffold-based classification based MW, logP, PSA, #HBA, #HBD … Filtration (Evaluator) 3. Use 17908 ligands in 2-D Classification based on 30 main aminoacids of TM cavities Calculation Ionisation (OpenEye) Ligands +/- More 160 rules : - Drug likeness (Lipinski rule of 5) - Rotatable bond number - Reactive and fluorescent groups … 3. Use 3-D conversion or/and database 4. Applications 4. Applications - To quantify identity and similarity of hGPCR transmembrane domains. - To quantify the structural similarity between of hGPCR active sites. - To assist library design by selection of user-defined scaffolds with annotated biological properties. Selection of drug-like compounds by topological, pharmacophoric properties. sc - PDB 1. Goal - Objective Collection of druggable protein binding sites. 2. Data presentation All data Filtering and analysis [1] 3. Use 1, 706 non redondant proteins 6, 415 active sites 4. Applications 2, 721 non redondant ligands - Quantify the similarity of active sites ... - Screening and reverse screening, docking Ligand, active site, protein files Conclusion . Design of high-content libraries with miscellaneous structures such as classifications, proteins, ligands makes it possible: - to highlight some hidden relations and correlations, - to build models more adapted to find new active compounds, - to increase the fields of investigations of ligands or scaffolds by decreasing skews. - and to improve and accelerate the search of new « hits », and « leads » at lower costs. Abbréviations: #HBA, number of H-bond acceptors; #HBD, number of H-bond donors; aa, aminoacid ; MW, molecular weight; PDB, Protein Data Bank; PSA, polar surface area ; TM, transmembran. Websites: scPDB http://bioinfo-pharma.u-strasbg.fr/scPDB ; hGPCR-lig http://bioinfo-pharma.u-strasbg.fr/hGPCR-lig Références: [1] Kellenberger, E., Muller, P., Schalon, C., Bret, G., Foata, N. and Rognan, D. (2006). sc-PDB: an Annotated Database of Druggable Binding Sites from the Protein Data Bank J. chem. Inf. Model. 46, 717-727. [2] Surgand, J.-S.; Rodrigo, J.; Kellenberger, E. and Rognan, D. (2006). A chemogenomic analysis of the transmembrane binding cavity of the human G-protein-coupled receptors. PROTEINS: Struct., Funct., and Bioinf., 62(2): 509-538 [3] Paul, N.; Kellenberger, E.; Bret, G.; Muller, P. and Rognan, D. (2004). Recovering the true targets of selective ligands by virtual screening of the Protein Data Bank.Proteins 54, 671-680.