Ligand-Supported Homology Modeling of G-Protein-Coupled Receptor Sites: Models Sufficient for Successful Virtual Screening
✍ Scribed by Andreas Evers; Gerhard Klebe
- Publisher
- John Wiley and Sons
- Year
- 2004
- Tongue
- English
- Weight
- 189 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0044-8249
No coin nor oath required. For personal study only.
✦ Synopsis
Recent advances in the various genome-sequencing projects have opened the floodgates to thousands of protein sequences that are possibly new targets for drug discovery. [1] Accordingly, computer techniques are required for the reliable prediction of protein structures accurate enough to serve as a platform for structure-based drug design. We have developed a new approach that models proteins by homology. Because the bound ligand molecules serve as restraints, more relevant geometries of protein binding sites result. [2] Initial homology models of the target protein are optimized iteratively by including information about bioactive ligands as spatial restraints (Figure 1).
Our approach, MOBILE (Modeling Binding Sites Including Ligand Information Explicitly), has been parameterized and validated by reproducing binding-site models of proteins for which the structure is actually known. Subsequently, we applied this method to the discovery of neurokinin-1 (NK1) receptor antagonists. A ligand with binding affinity in the submicromolar range was found for this protein, which belongs to the family of G-protein-coupled receptors (GPCRs).
In structure-based drug design, knowledge of the threedimensional structure of a target protein is of utmost importance. It is either determined by X-ray crystallography or by NMR spectroscopy. However, the rate at which protein sequences are presently being discovered exceeds by far the rate at which protein structures can be determined experimentally. Thus, for a considerable number of putative drug targets, the three-dimensional structure will not be readily available. In such cases the most reliable computer-based technique for generating a three-dimensional protein structure is homology modeling. [7] However, homology modeling only considers information available from the related protein structures. The model remains at a rather approximate level if in the target protein several amino acids of the active site are replaced with respect to those in the template protein(s). Usually, in a drug-design project, binding data about ligands become available, for example, from high-throughput screening, before the spatial structure is elucidated. In due course, ligand-based structure-activity relationships are established (by 3D QSAR methods) [8] to pinpoint features responsible for trends in the binding affinity. These methods generate some kind of a generalized blueprint of the putative binding pocket of the target protein without giving explicit information about its actual composition. These methods further support ligand optimization; however, they are limited with respect to the design of novel or alternative molecular skeletons. Our new method tries to combine the advantages of both homology modeling and ligand-based 3D QSAR analysis.
In a real-life test scenario we applied MOBILE to the neurokinin-1 (NK1) receptor belonging to the superfamily of GPCRs mediating responses to, for example, visual, olfactory, hormonal, and neurotransmitter signals. GPCRs are one of the most relevant target families in small-molecule drug design. Currently, 50 % of all marketed drugs address GPCRs. [9] Since GPCRs are membrane-bound proteins, their expression, purification, crystallization, and structure determination remain difficult. So far, only the structure of bovine rhodopsin could be determined with sufficiently high resolution. [10] Its crystal structure serves as a template reference for homology modeling of all other members of the GPCR family. Because of the limited accuracy, drug discovery based on virtual screening with rhodopsin-based GPCR models has not yet been reported in literature. Instead, successful computer-aided drug discovery for GPCRs was achieved by applying ligand-based virtual screening techniques. [11] A first step towards drug discovery with rhodopsin-based protein models was attempted by Bissantz et al., [12] who recently demonstrated that their homology models of the dopamine D3, muscarinic M1, and vasopressin V1a receptors [*] A.