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Didier Rognan

Publications and source records attributed to Didier Rognan.

28 records · Page 2Linked to original sources
PubMed2003

Identification of the binding sites of the SR49059 nonpeptide antagonist into the V1a vasopressin receptor using sulfydryl-reactive ligands and cysteine mutants as chemical sensors.

To identify the binding site of the human V1a vasopressin receptor for the selective nonpeptide antagonist SR49059, we have developed a site-directed irreversible labeling strategy that combines mutagenesis of the receptor and use of sulfydryl-reactive ligands. Based on a three-dimensional model of the antagonist docked into the receptor, hypothetical ligand-receptor interactions were investigated by replacing the residues potentially involved in the binding of the antagonist into cysteines and designing analogues of SR49059 derivatized with isothiocyanate or alpha-chloroacetamide moieties. The F225C, F308C, and K128C mutants of the V1a receptor were expressed in COS-7 or Chinese hamster ovary cells, and their pharmacological properties toward SR49059 and its sulfydryl-reactive analogues were analyzed. We demonstrated that treatment of the F225C mutant with the isothiocyanate-derivative compound led to dose-dependent inhibition of the residual binding of the radio-labeled antagonist [125I]HO-LVA. This inhibition is probably the consequence of a covalent irreversible chemical modification, which is only possible when close contacts and optimal orientations exist between reactive groups created both on the ligand and the receptor. This result validated the three-dimensional model hypothesis. Thus, we propose that residue Phe225, located in transmembrane domain V, directly participates in the binding of the V1a-selective nonpeptide antagonist SR49059. This conclusion is in complete agreement with all our previous data on the definition of the agonist/antagonist binding to members of the oxytocin/vasopressin receptor family.

Amino Acid Sequence↗
PubMed2003

Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets?

The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).

Adrenergic beta-2 Receptor Agonists↗
PubMed2002

Probing the cysteine-34 position of endogenous serum albumin with thiol-binding doxorubicin derivatives. Improved efficacy of an acid-sensitive doxorubicin derivative with specific albumin-binding properties compared to that of the parent compound.

We have recently proposed a macromolecular prodrug strategy for improved cancer chemotherapy based on two features (Kratz, F.; et al. J. Med. Chem 2000, 43, 1253-1256.): (a) rapid and selective binding of thiol-reactive prodrugs to the cysteine-34 position of endogenous albumin after intravenous administration and (b) release of the albumin-bound drug in the acidic environment at the tumor site due to the incorporation of an acid-sensitive bond between the drug and the carrier. To investigate this therapeutic strategy in greater depth, four (maleinimidoalkanoyl)hydrazone derivatives of doxorubicin were synthesized differing in the length of the aliphatic spacer (1, -(CH(2))(2)-; 2, -(CH(2))(3)-; 3, -(CH(2))(5)-; 4, -(CH(2))(7)-). The albumin-binding doxorubicin prodrugs, especially the (6-maleimidocaproyl)hydrazone derivative of doxorubicin (3), are rapidly and selectively bound to the cysteine-34 position of endogenous albumin. 3 was distinctly superior to the parent compound doxorubicin in three animal tumor models (RENCA, MDA-MB 435, and MCF-7) with respect to antitumor efficacy and toxicity.

Animals↗
PubMed2002

Molecular mimicry of an HLA-B27-derived ligand of arthritis-linked subtypes with chlamydial proteins.

HLA-B27 is strongly associated with spondyloarthropathies, including ankylosing spondylitis and reactive arthritis. The latter disease is triggered by various Gram-negative bacteria. A dodecamer derived from the intracytoplasmic tail of HLA-B27 was a natural ligand of three disease-associated subtypes (B*2702, B*2704, and B*2705) but not of two (B*2706 and B*2709), weakly or not associated to spondyloarthropathy. This peptide was strikingly homologous to protein sequences from arthritogenic bacteria, particularly to a region of the DNA primase from Chlamydia trachomatis. A synthetic peptide with this bacterial sequence bound in vitro disease-associated subtypes equally as the natural B27-derived ligand. The chlamydial peptide was generated by the 20 S proteasome from a synthetic 28-mer with the sequence of the corresponding region of the bacterial DNA primase. Molecular modeling suggested that the B27-derived and chlamydial peptides adopt very similar conformations in complex with B*2705. The results demonstrate that an HLA-B27-derived peptide mimicking arthritogenic bacterial sequences is a natural ligand of disease-associated HLA-B27 subtypes and suggest that the homologous chlamydial peptide might be presented by HLA-B27 on Chlamydia-infected cells.

Amino Acid Sequence↗
PubMed2002

ConsDock: A new program for the consensus analysis of protein-ligand interactions.

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution.

Animals↗
PubMed2002

Recovery of known T-cell epitopes by computational scanning of a viral genome.

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.

Alleles↗
PubMedDate not supplied

High-throughput modeling of human G-protein coupled receptors: amino acid sequence alignment, three-dimensional model building, and receptor library screening.

The current study describes the development of a computer package (GPCRmod) aimed at the high-throughput modeling of the therapeutically important family of human G-protein coupled receptors (GPCRs). GPCRmod first proposes a reliable alignment of the seven transmembrane domains (7 TMs) of most druggable human GPCRs based on pattern/motif recognition for each of the 7 TMs that are considered independently. It then converts the alignment into knowledge-based three-dimensional (3-D) models starting from a set of 3-D backbone templates and two separate rotamer libraries for side chain positioning. The 7 TMs of 277 human GPCRs have been accurately aligned, unambiguously clustered in three different classes (rhodopsin-like, secretin-like, metabotropic glutamate-like), and converted into high-quality 3-D models at a remarkable throughput (ca. 3s/model). A 3-D GPCR target library of 277 receptors has consequently been setup. Its utility for "in silico" inverse screening purpose has been demonstrated by recovering among top scorers the receptor of a selective GPCR antagonist as well as the receptors of a promiscuous antagonist. The current GPCR target library thus constitutes a 3-D database of choice to address as soon as possible the "virtual selectivity" profile of any GPCR antagonist or inverse agonist in an early hit optimization process.

Amino Acid Sequence↗
PubMedDate not supplied

Assessing the scaffold diversity of screening libraries.

Medicinal chemists have traditionally realized assessments of chemical diversity and subsequent compound acquisition, although a recent study suggests that experts are usually inconsistent in reviewing large data sets. To analyze the scaffold diversity of commercially available screening collections, we have developed a general workflow aimed at (1) identifying druglike compounds, (2) clustering them by maximum common substructures (scaffolds), (3) measuring the scaffold diversity encoded by each screening collection independently of its size, and finally (4) merging all common substructures in a nonredundant scaffold library that can easily be browsed by structural and topological queries. Starting from 2.4 million compounds out of 12 commercial sources, four categories of libraries could be identified: large- and medium-sized combinatorial libraries (low scaffold diversity), diverse libraries (medium diversity, medium size), and highly diverse libraries (high diversity, low size). The chemical space covered by the scaffold library can be searched to prioritize scaffold-focused libraries.

Chemistry, Pharmaceutical↗
PubMedDate not supplied

sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank.

The sc-PDB is a collection of 6 415 three-dimensional structures of binding sites found in the Protein Data Bank (PDB). Binding sites were extracted from all high-resolution crystal structures in which a complex between a protein cavity and a small-molecular-weight ligand could be identified. Importantly, ligands are considered from a pharmacological and not a structural point of view. Therefore, solvents, detergents, and most metal ions are not stored in the sc-PDB. Ligands are classified into four main categories: nucleotides (< 4-mer), peptides (< 9-mer), cofactors, and organic compounds. The corresponding binding site is formed by all protein residues (including amino acids, cofactors, and important metal ions) with at least one atom within 6.5 angstroms of any ligand atom. The database was carefully annotated by browsing several protein databases (PDB, UniProt, and GO) and storing, for every sc-PDB entry, the following features: protein name, function, source, domain and mutations, ligand name, and structure. The repository of ligands has also been archived by diversity analysis of molecular scaffolds, and several chemoinformatics descriptors were computed to better understand the chemical space covered by stored ligands. The sc-PDB may be used for several purposes: (i) screening a collection of binding sites for predicting the most likely target(s) of any ligand, (ii) analyzing the molecular similarity between different cavities, and (iii) deriving rules that describe the relationship between ligand pharmacophoric points and active-site properties. The database is periodically updated and accessible on the web at http://bioinfo-pharma.u-strasbg.fr/scPDB/.

Algorithms↗