Introduction

1. Design of high-content and focused libraries to improvethe development of new active compounds in the framework of the rational drug discoveryNicolas Foata, Esther Kellenberger, Mireille Krier, Pascal Muller, Claire Schalon, Jean-Sébastien Surgand, Guillaume Bret and Didier RognanCNRS UMR 7175 – LC1 Bioinformatics of the drugF-67400 ILLKIRCH-GRAFFENSTADEN, FRANCEnicolas.foata@pharma.u-strasbg.fr: +33 (0)3-90-24-42-24 Introduction 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. 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. Databases Filtration of the entries 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 2. Data presentation 1 .. 23 2-D catalogues Suppliers Receptors Ligands 369 human GPCRs 17908 ligandsin 2-D Compilation – Cleaning (PipeLine Pilot) [2] Classification based on 30 main aminoacids of TM cavities Scaffold-based classification based MW, logP, PSA, #HBA, #HBD … Steps Calculation More 160 rules : - Drug likeness (Lipinski rule of 5) - Rotatable bond number - Reactive and fluorescent groups … Filtration (Evaluator) 3. Use Ionisation (OpenEye) + / - 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 [1] 3. Use Collection of druggable protein binding sites. 1, 706 non redondant proteins 2. Data presentation All data 6, 415 active sites 4. Applications Filtering and analysis 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.