Transcript sc-PDB: an annotated database of druggable binding sites from the

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.