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Review
. 2007 Sep;152(1):38-52.
doi: 10.1038/sj.bjp.0707307. Epub 2007 May 29.

Chemogenomic approaches to rational drug design

D Rognan  1
Affiliations
Review

Chemogenomic approaches to rational drug design

D Rognan. Br J Pharmacol. 2007 Sep.

Abstract

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.

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Figures

Figure 1
Figure 1
Examples of molecular descriptors for small-molecular-weight ligands
Figure 2
Figure 2
Various representations of a protein using 1-D to 3-D properties.
Figure 3
Figure 3
(a) Deriving and (b) comparing protein–ligand complexes by molecular interaction fingerprints. ‘0' and ‘1' digits are replaced by colour-coded squares for the ease of comparison (blue, hydrophobic interactions; green, aromatic interactions; red, hydrogen bonds).
Figure 4
Figure 4
Structure–activity relationship homology flowchart.
Figure 5
Figure 5
Permissivity of the 3H-1,4-benzodiazepin-2-one scaffold (in gray) across various targets. 1: Ro-5–3335, HIV-1 Tat inhibitor; 2: Diazepam, GABA-A receptor ligand; 3: 231023: farnesyltransferase inhibitor; 4: CI-1044, phosphodiesterase 4 inhibitor; 5: pranazepide, cholescystokinine (CCK) receptor antagonist; 6: BZ-423, F1F0 ATPase inhibitor, 7:171644, oxytocin receptor antagonist, 8: 309060: β-γ secretase inhibitor; 9: 278588: Stat5 agonist; 10: 276345: KVs channel blocker.
Figure 6
Figure 6
Human GPCRs targeted by the orthoalkoxy-N-phenylpiperazine privileged structure (http://bioinfo-pharma.u-strasbg.fr/hGPCRLig/). Remarkably, no other proteins ever co-crystallized with drug-like compounds are able to recognize this substructure (http://bioinfo-pharma.u-strasbg.fr/scPDB/).
Figure 7
Figure 7
In silico target fishing approaches.
Figure 8
Figure 8
Sequence-based comparison of targets exemplified by human adenosine receptors (Surgand et al., 2006). (a) Selection of key cavity-lining residues and (b) Clustering according to residue conservation.
Figure 9
Figure 9
Molecular interaction field (MIF)-based clustering of targets.
Figure 10
Figure 10
Screening a non-redundant subset of 1060 binding sites form the sc-PDB target library (Kellenberger et al., 2006) for ligand binding sites similar to that of GPR30 for 4-hydroxy-tamoxifen. (a) Ranking sc-PDB entries by decreasing similarity (ranging from 1 to 0) to the GPR30 4-hydroxy-tamoxifen binding site (extrapolated from a consensus list of 30 residues delimiting a canonical non-peptide binding site for most GPCR ligands as proposed by Surgand et al. (2006). The 3ert sc-PDB entry (4-hydroxy-tamoxifen binding site in the estrogen receptor α) is ranked second among 1060 investigated binding site. (b) Predicted alignment of GPR30 (blue) to ER-α binding site (green) for 4-OH tamoxifen (white ball and sticks). Both binding sites present a water-accessible negatively charged residue and a buried hydrophobic region (similarity index of 0.79 according to the SiteAlign program (Surgand et al., 2006)).
Figure 11
Figure 11
Querying the sc-PDB chemogenomic database (http://bioinfo-pharma.u-strasbg.fr/scPDB) for rule-of-five compliant (Lipinski et al., 2001) small molecular weight fragments (MW <300, clogP <3, H-bond donor count <3, H-bond acceptor count <6) co-crystallized with protein kinases.
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