Didier Rognan

Comparative Evaluation of Eight Docking Tools for Docking and Virtual Screening Accuracy

Eight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single‐ligand docking or database screening have been compared for their propensity to recover the X‐ray pose of 100 small‐molecular‐weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen “drug‐like” molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein‐binding site that generally lead to docking/scoring inaccuracies. Proteins 2004.

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 Å‐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). Proteins 2003;50:5–25.

Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.

A Chemogenomic Analysis of the Transmembrane Binding Cavity of Human G-Protein-Coupled Receptors

The amino acid sequences of 369 human nonolfactory G‐protein‐coupled receptors (GPCRs) have been aligned at the seven transmembrane domain (TM) and used to extract the nature of 30 critical residues supposed—from the X‐ray structure of bovine rhodopsin bound to retinal—to line the TM binding cavity of ground‐state receptors. Interestingly, the clustering of human GPCRs from these 30 residues mirrors the recently described phylogenetic tree of full‐sequence human GPCRs (Fredriksson et al., Mol Pharmacol 2003;63:1256–1272 ) with few exceptions. A TM cavity could be found for all investigated GPCRs with physicochemical properties matching that of their cognate ligands. The current approach allows a very fast comparison of most human GPCRs from the focused perspective of the predicted TM cavity and permits to easily detect key residues that drive ligand selectivity or promiscuity. Proteins 2006.

Identification of a low–molecular weight TrkB antagonist with anxiolytic and antidepressant activity in mice

The neurotrophin brain-derived neurotrophic factor (BDNF) and its receptor tropomyosin-related kinase B (TrkB) have emerged as key mediators in the pathophysiology of several mood disorders, including anxiety and depression. However, therapeutic compounds that interact with TrkB receptors have been difficult to develop. Using a combination of structure-based in silico screening and high-capacity functional assays in recombinant and neuronal cells, we identified a low-molecular weight TrkB ligand (ANA-12) that prevented activation of the receptor by BDNF with a high potency. ANA-12 showed direct and selective binding to TrkB and inhibited processes downstream of TrkB without altering TrkA and TrkC functions. KIRA-ELISA analysis demonstrated that systemic administration of ANA-12 to adult mice decreased TrkB activity in the brain without affecting neuronal survival. Mice administered ANA-12 demonstrated reduced anxiety- and depression-related behaviors on a variety of tests predictive of anxiolytic and antidepressant properties in humans. This study demonstrates that structure-based virtual screening strategy can be an efficient method for discovering potent TrkB-selective ligands that are active in vivo. We further propose that ANA-12 may be a valuable tool for studying BDNF/TrkB signaling and may constitute a lead compound for developing the next generation of therapeutic agents for the treatment of mood disorders.

Delineating a CA2+ binding pocket within the venus flytrap module of the human calcium sensing receptor

The Ca2+-sensing receptor (CaSR) belongs to the class III G-protein-coupled receptors (GPCRs), which include receptors for pheromones, amino acids, sweeteners, and the neurotransmitters glutamate and γ-aminobutyric acid (GABA). These receptors are characterized by a long extracellular amino-terminal domain called a Venus flytrap module (VFTM) containing the ligand binding pocket. To elucidate the molecular determinants implicated in Ca2+ recognition by the CaSR VFTM, we developed a homology model of the human CaSR VFTM from the x-ray structure of the metabotropic glutamate receptor type 1 (mGluR1), and a phylogenetic analysis of 14 class III GPCR VFTMs. We identified critical amino acids delineating a Ca2+ binding pocket predicted to be adjacent to, but distinct from, a cavity reminiscent of the binding site described for amino acids in mGluRs, GABA-B receptor, and GPRC6a. Most interestingly, these Ca2+-contacting residues are well conserved within class III GPCR VFTMs. Our model was validated by mutational and functional analysis, including the characterization of activating and inactivating mutations affecting a single amino acid, Glu-297, located within the proposed Ca2+ binding pocket of the CaSR and associated with autosomal dominant hypocalcemia and familial hypocalciuric hypercalcemia, respectively, genetic diseases characterized by perturbations in Ca2+ homeostasis. Altogether, these data define a Ca2+ binding pocket within the CaSR VFTM that may be conserved in several other class III GPCRs, thereby providing a molecular basis for extracellular Ca2+ sensing by these receptors.

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/.

Selective Structure-Based Virtual Screening for Full and Partial Agonists of the β2 Adrenergic Receptor

The recently solved high-resolution X-ray structure of the beta2 adrenergic receptor has been challenged for its ability to discriminate inverse agonists/antagonists from partial/full agonists. Whereas the X-ray structure of the ground state receptor was unsuitable to distinguish true ligands with different functional effects, modifying this structure to reflect early conformational events in receptor activation led to a receptor model able to selectively retrieve full and partial agonists by structure-based virtual screening. The use of a topological scoring function based on molecular interaction fingerprints was shown to be mandatory to properly rank docking poses and achieve acceptable enrichments for partial and full agonists only.

Structure-Based Approaches to Target Fishing and Ligand Profiling

Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time‐consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure‐based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

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 Å 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. Proteins 2002;47:521–533.