Molecular modeling of the second extracellular loop of G-protein coupled receptors and its implication on structure-based virtual screening

The current study describes the validation of high-throughput modeling procedures for the construction of the second extracellular loop (ecl2) of all nonolfactory human G Protein-coupled receptors. Our modeling flowchart is based on the alignment of essential residues determining the particular ecl2 fold observed in the bovine rhodopsin (bRho) crystal structure. For a set of GPCR targets, the dopamine D2 receptor (DRD2), adenosine A3 receptor (AA3R), and the thromboxane A2 receptor (TA2R), the implications of including ecl2 atomic coordinates is evaluated in terms of structure-based virtual screening accuracy: the suitability of the 3D models to distinguish between known antagonists and randomly chosen decoys using automated docking approaches. The virtual screening results of different models describing increasingly exhaustive receptor representations (seven helices only, seven helices and ecl2 loop, full model) have been compared. Explicit modeling of the ecl2 loop was found to be important in only one of three test cases whereas a loopless model was shown to be accurate enough in the two other receptors. An exhaustive comparison of ecl2 loops of 365 receptors to that of bRho suggests that explicit ecl2 loop modeling should be reserved to receptors where loop building can be guided by experimental restraints.

Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.

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/.