The long range goals of this research are to identify affinity labels based on peptide ligands for opioid receptors, and to use these compounds as tools to obtain detailed structural information on receptor-ligand interactions. Identification of the points where affinity labels attach covalently to their receptors will provide direct information about specific receptor-ligand interactions. This information, which will be complimentary to results obtained for nonpeptide ligands and from molecular biological and computational techniques, can be used to evaluate and improve current models for receptor-ligand interactions. A more detailed understanding of how these ligands interact with their receptors at a molecular level will provide valuable information for the design of new therapeutic agents, including analgesics with fewer side effects and agents for treating substance abuse (including alcohol and cocaine abuse as well as opiate abuse). The proposed research involves two phases.
The specific aim of the first phase in this competitive renewal application is to prepare new peptide-based affinity labels for opioid receptors and to evaluate their ability to bind to their receptors in an irreversible manner. These peptide derivatives were chosen to answer a series of questions concerning the interactions of key pharmacophoric groups on the peptides with the receptors. The derivatives synthesized will include analogues of endogenous opioid peptides and peptide antagonists, and will involve exploration of different positions in the peptide for incorporation of the affinity label. Peptides that exhibit wash-resistant inhibition of binding will be utilized in the second phase of the research. Additional functionalities will be incorporated into the peptides that will facilitate rapid receptor isolation and characterization. The affinity labeled receptors will be subjected to proteolytic digestion and the point of attachment of the affinity label determined by mass spectrometric techniques. This information can then be used in combination with computational techniques to refine current models for receptor ligand interactions.
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