Accueil > Événements > Séminaires

Séminaire Chimie ED459

Using consensus-shape clustering to identify promiscuous ligands and protein targets

Dr. Violeta I. Perez-Nueno (LORIA, INRIA Nancy Grand Est)

publié le

Le Jeudi 24 novembre 2011 à 13h45
UM2, salle de cours SC-16.01

(co-authors : Violeta I. Perez-Nueno, David W. Ritchie)

In recent years, polypharmacology has increasingly gained attention. Pharmaceutical companies have discovered more and more cases of multiple drugs binding to a given target (promiscuous targets) and conversely, a given drug binding to more than one molecular target (promiscuous ligands). This is clearly of great importance when considering drug side-effects.

We previously introduced the notion of spherical harmonic-based « consensus shapes » to help deal with these targets [1]. This approach allows one or more “pseudo-molecules” to be created and used as VS query structures. In a previous study on some 15 diverse families of CCR5 inhibitors, which could not all be superposed together, we found that the ligands may be clustered into four main super-consensus families, and we predicted that these might bind within three sub-sites in the CCR5 extra-cellular pocket in a manner consistent with experimental site-directed mutagenesis information and other computational studies. Thus, consensus clustering seems to offer a straightforward way to understand how multiple ligands might distribute themselves within a given binding site.

Here we present our shape-based approach which uses spherical harmonic (SH) representations [2,3] to compare molecular surfaces very efficiently. We have applied our approach to compare targets by the SH similarity of their ligands and also of their binding pockets. This allows promiscuous ligands and targets to be predicted very rapidly [4,5,6]. We present details of our approach applied to a subset of the MDL Drug Data Report (MDDR) database containing 65,367 compounds distributed over 249 diverse pharmacological targets. The similarity of each ligand to each target’s ligand set is quantified and used to predict promiscuity. Our results indicate that our approach is a useful way to study polypharmacology relationships, and it could provide a novel way to propose new targets for drug repositioning.


1. Pérez-Nueno, V. I. ; Ritchie, D. W. ; Borrell, J. I. ; Teixidó, J. Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape based virtual screening approach : Further evidence for multiple binding sites within the CCR5 extracellular pocket. J. Chem. Inf. Model. 2008, 48, 2146-2165.
2. Lin J, Clark T : An Analytical, Variable Resolution, Complete Description of Static Molecules and Their Intermolecular Binding Properties. J. Chem. Inf. Model. 2005, 45, 1010-1016.
3. Ritchie DW, Kemp GJL : Protein Docking Using Spherical Polar Fourier Correlations. Proteins : Struct. Func. Genet. 2000, 39, 178-194.
4. Pérez-Nueno, V. I. ; Venkatraman, V. ; Mavridis, L. ; Clark, T. ; Ritchie, D.W. Using spherical harmonic surface property representations for ligand-based virtual screening. Molecular Informatics 2011, 30, 151-159.
5. Pérez-Nueno, V. I. ; Ritchie, D.W. Using Consensus-Shape Clustering to Identify Promiscuous Ligands and Protein targets and to Choose the Right Query for Shape-Based Virtual Screening. J. Chem. Inf. Model. 2011, 51, 1233-1248.
6. Pérez-Nueno, V. I. ; Venkatraman, V. ; Mavridis, L. ; Ritchie, D.W. Predicting drug polypharmacology using a novel surface property similarity-based approach. Journal of Cheminformatics 2011, 3 (Suppl 1), O19.

Contact local IBMM : Dr. Alain Chavanieu


Ajouter un événement iCal