1. Design of high-content and focused libraries to improve the development of new active compounds in the framework of the rational drug discovery 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 nicolas.foata@pharma.u-strasbg.fr: +33 (0)3-90-24-42-24 nicolas.foata@pharma.u-strasbg.fr Introduction 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-lighttp://bioinfo-pharma.u-strasbg.fr/scPDBhttp://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. Bioinfo Filtration of the entries Nowadays, new communication and information technologies give access to an important quantity of specific data. However, in chemoinformatics, such data are often too generic, focused on a single application field and not suited to a precise problem. Consequently, we generated and conceived several databases allowing the crossing of miscellaneous information. The first one called Bioinfo, is a library of 1.8 million commercially available drug-like compounds that can be use in the framework of the in silico screening. Databases hGPCR - lig Conclusion. 1. Goal - Objective 4. Applications 2. Data presentation 3. Use 1. Goal - Objective 2. Data presentation Library setting up of commercially available drug-like compounds. Bank of 3-D human G-Protein Coupled Receptor models and their known ligands. 3. Use sc - PDB 1. Goal - Objective 2. Data presentation 3. Use 4. Applications + / - Suppliers Compilation – Cleaning (PipeLine Pilot) Filtration (Evaluator) Ionisation (OpenEye) 4. Applications Calculation The second one named "screening-Protein Data Bank" (sc-PDB) is a collection of 6415 druggable binding sites from proteins whose x-ray structure has been deposited in the Protein Data Bank (PDB). The last one, human G-Protein Coupled Receptors & ligands (hGPCR-lig), is a collection of human GPCR (369) and their ligands (17908), also classified according to the diversity of receptor binding sites and their ligands, respectively. 1.. 23 Receptors Ligands 369 human GPCRs Classification based on 30 main aminoacids of TM cavities 17908 ligands in 2-D Scaffold-based classification based Collection of druggable protein binding sites. All data Filtering and analysis Ligand, active site, protein files 2-D catalogues MW, logP, PSA, #HBA, #HBD … More 160 rules : - Drug likeness (Lipinski rule of 5) - Rotatable bond number - Reactive and fluorescent groups … 3-D conversion or/and database - 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. - Quantify the similarity of active sites... - Screening and reverse screening, docking Steps [2] [1] - 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. 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, Selection of drug-like compounds by topological, pharmacophoric properties. 6, 415 active sites 1, 706 non redondant proteins 2, 721 non redondant ligands