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19th EuroQSAR

Presentation Abstract

Monday, August 27th, 9:50 am
Track: Pharmacophore-based Screening and Design

Presenter: Didier Rognan, CNRS
Authors: Jamel Meslamani,1 Jiabo Li,2 Jon Sutter,2 Adrian Stevens,3 Hugues-Olivier Bertrand,4 Jean-Guy Delcros,5 Patrick Mehlen,5 Jean-Phillipe Leyris,6 Jean Valmier,6 and Didier Rognan.1*

1 Laboratoire d'Innovation Thérapeutique, UMR7200 Université de Strasbourg/CNRS, 74 route du Rhin, 67400 Illkirch, France
2 Accelrys, Inc., 10188 Telesis Court, Suite 100, San Diego, CA 92121, U.S.A.
3 Accelrys Ltd., 334 Cambridge Science Park, Cambridge CB4 OWN, England
4 Accelrys SARL, Parc Club Orsay Université, 20 Rue Jean Rostand, 91898 Orsay Cédex, France
5 UMR INSERM U1052/CNRS 5286, Centre Léon Bérard, 28 rue Laennec, 69008 Lyon, France
6 INSERM U1051, Institut des Neurosciences de Montpellier, Montpellier, France

Receptor-Ligand Pharmacophores: A Novel Structure-based Screening Weapon for Ligand Profiling & Discovery of Protein-protein Interface Inhibitors

We herewith present PharmaDB [1], a collection of 68,000 pharmacophores derived from 8,000 high resolution protein-ligand complexes from the sc-PDB dataset [2]. A maximum of 10 pharmacophores (3-6 features) per complex are automatically derived from the simple inspection of receptor-ligand atomic coordinates, and further ranked by expected decreasing selectivity using a genetic function approximation.

In a first application, receptor-ligand pharmacophore search was compared to ligand-centric (2-D and 3-D similarity searches) and docking methods in profiling a set of 157 diverse ligands against a panel of 2,556 unique targets of known X-ray structure. As expected, ligand-based methods outperformed, in most of the cases, structure-based approaches in ranking the true targets among the top 1% scoring entries. However, we could identify ligands for which only a single method was successful. Receptor-ligand-based pharmacophore search is notably a very fast and reliable alternative to docking in which constraints are explicitly defined, and which generates high-quality poses.

In a second scenario, the method was applied (in parallel to docking) to screen a collection of 3 million commercially-available compounds for finding the very first inhibitors of two independent protein-protein interfaces (PPI) [3,4]. Strikingly, only protein-protein pharmacophore mapping was able to identify micromolar hits which were later confirmed by binding and functional assays. Although more prospective examples are required to derive general rules, protein-protein pharmacophore search appears a very promising and robust tool to find PPI inhibitors.
The PharmaDB collection will be appended very soon to the upcoming release of DiscoveryStudio.

  1. Meslamani J, Li J, Sutter J, Stevens A, Bertrand HO, Rognan D. Protein-Ligand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling. J Chem Inf Model. 2012; 52: 943–955.
  2. Meslamani J, Rognan D, Kellenberger E. sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins. Bioinformatic.s2011; 27: 1324-1326.
  3. Bouzas-Rodriguez J, Cabrera JR, Delloye-Bourgeois C, Ichim G, Delcros JG, Raquin MA, Rousseau R, Combaret V, Bénard J, Tauszig-Delamasure S, Mehlen P. Neurotrophin-3 production promotes human neuroblastoma cell survival by inhibiting TrkC-induced apoptosis. J Clin Invest. 2010; 120: 850-858.
  4. Verstraete K, Vandriessche G, Januar M, Elegheert J, Shkumatov AV, Desfosses A, Van Craenenbroeck K, Svergun DI, Gutsche I, Vergauwen B, Savvides SN. Structural insights into the extracellular assembly of the hematopoietic Flt3 signaling complex. Blood. 2011; 118: 60-58.


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