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A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist.

Nidhi Singh, Gwénaël Chevé, David M Ferguson, Christopher R Mccurdy

Journal of computer-aided molecular design 2006 DOI: 10.1007/s10822-006-9067-x (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or computational modeling study Peer reviewed
Topics Salvia divinorum
Key points The authors report that a pharmacophore model based on salvinorin A derivatives and a bovine rhodopsin-based agonist-bound hKOP receptor model were both generated and validated, with docking scores correlating reasonably with experimental pKi values. They propose that integrating these two independently derived models could enable rapid, cost-effective identification of new lead compounds for GPCR drug discovery.

Abstract

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK (i) values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

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