Design of high-content and focused libraries to improveDesign of high-content and focused libraries to improvethe development of new active compounds the development of new active compounds

in the framework of the rational drug discoveryin the framework of the rational drug discoveryNicolas Foata, Esther Kellenberger, Mireille Krier, Pascal Muller, Claire Schalon, Jean-Sébastien Surgand, Guillaume Bret and Didier Rognan

CNRS UMR 7175 – LC1 Bioinformatics of the drugF-67400 ILLKIRCH-GRAFFENSTADEN, FRANCE

nicolas.foata@pharma.u-strasbg.fr: +33 (0)3-90-24-42-24

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-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 - PDB1. 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 ligandsin 2-D

Scaffold-based classification

based

Collection of druggable protein binding sites.

All data

Filtering andanalysis

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

Ste

ps

[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 redondantproteins

2, 721 non redondantligands