Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening.

Melanin-concentrating hormone receptor 1 (MCH-R1) is a G-protein-coupled receptor (GPCR) and a target for the development of therapeutics for obesity. The structure-based development of MCH-R1 and other GPCR antagonists is hampered by the lack of an available experimentally determined atomic structure. A ligand-steered homology modeling approach has been developed (where information about existing ligands is used explicitly to shape and optimize the binding site) followed by docking-based virtual screening. Top scoring compounds identified virtually were tested experimentally in an MCH-R1 competitive binding assay, and six novel chemotypes as low micromolar affinity antagonist "hits" were identified. This success rate is more than a 10-fold improvement over random high-throughput screening, which supports our ligand-steered method. Clearly, the ligand-steered homology modeling method reduces the uncertainty of structure modeling for difficult targets like GPCRs.

Structure-based development of target-specific compound libraries.

The success or failure of a small-molecule drug discovery project ultimately lies in the choice of the scaffolds to be screened -- chosen from among the many millions of available compounds. Therefore, the methods used to design compound screening libraries are key for the development of new drugs that target a wide range of diseases. Currently, there is a trend towards the construction of receptor-structure-based focused libraries. Recent advances in high-throughput computational docking, NMR and crystallography have facilitated the development of these libraries. A structure-based target-specific library can save time and money by reducing the number of compounds to be experimentally tested, also improving the drug discovery success rate by identifying more-potent and specific binders.

Structure-based identification of binding sites, native ligands and potential inhibitors for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are the largest family of cell-surface receptors involved in signal transmission. Drugs associated with GPCRs represent more than one fourth of the 100 top-selling drugs and are the targets of more than half of the current therapeutic agents on the market. Our methodology based on the internal coordinate mechanics (ICM) program can accurately identify the ligand-binding pocket in the currently available crystal structures of seven transmembrane (7TM) proteins [bacteriorhodopsin (BR) and bovine rhodopsin (bRho)]. The binding geometry of the ligand can be accurately predicted by ICM flexible docking with and without the loop regions, a useful finding for GPCR docking because the transmembrane regions are easier to model. We also demonstrate that the native ligand can be identified by flexible docking and scoring in 1.5% and 0.2% (for bRho and BR, respectively) of the best scoring compounds from two different types of compound database. The same procedure can be applied to the database of available chemicals to identify specific GPCR binders. Finally, we demonstrate that even if the sidechain positions in the bRho binding pocket are entirely wrong, their correct conformation can be fully restored with high accuracy (0.28 A) through the ICM global optimization with and without the ligand present. These binding site adjustments are critical for flexible docking of new ligands to known structures or for docking to GPCR homology models. The ICM docking method has the potential to be used to "de-orphanize" orphan GPCRs (oGPCRs) and to identify antagonists-agonists for GPCRs if an accurate model (experimentally and computationally validated) of the structure has been constructed or when future crystal structures are determined.

Preparation and refinement of model protein-ligand complexes.

The formation of ligand-protein complexes are critical for the correct functioning of a cell. The prediction of these interactions is important for our understanding of how the cell works and for the development of new drug molecules. Homology modeling is a method for predicting the structure of a protein based on a crystal structure template. Once a model of the protein is complete, a ligand-docking algorithm predicts the ligand-protein model interaction by searching for the best steric and energetically favorable fit. A refinement of the ligand-binding pocket improves the predicted interactions by considering the flexible nature of the ligand-binding pocket. In this chapter, we describe, from first principles, methods to identify and prepare the ligand-binding pocket in a protein model, to dock the ligand, and refine the resulting complex.

Ligand docking and structure-based virtual screening in drug discovery.

Ligand-docking-based methods are starting to play a critical role in lead discovery and optimization, thus resulting in new 'drug-candidates'. They offer the possibility to go beyond the pool of existing active compounds, and thus find novel chemotypes. A brief tutorial on ligand docking and structure-based virtual screening is presented highlighting current problems and limitations, together with the most recent methodological and algorithmic developments in the field. Recent successful applications of docking-based tools for hit discovery, lead optimization and target-biased library design are also presented. Special consideration is devoted to ongoing efforts to account for protein flexibility in structure-based virtual screening.