Structure-based discovery is a proven method for identifying novel compounds that are potent and selective for a given drug target. However, as this method relies on the knowledge of a drug target?s structure, we are limited by the availability of known structures. G-protein coupled receptors (GPCRs) are the most heavily targeted family of proteins, and yet, due to their nature as membrane proteins, our structural knowledge of these proteins is limited. In order to leverage structure-based discovery for understudied GPCRs, we must use homology models for virtual screens. There are examples of success when using homology models for prospective screens, but there are many cases that have failed with causes unknown. In order to devise a set of rules that can more effectively guide the use of homology models in virtual screens we must analyze each input to the modeling and docking process systematically. Important unknowns include the accuracy of a homology model with respect to a given crystal structure, the role of known actives, both in number and activity, that can be used to discriminate effective models, and the ability to automate hit picking after a 400 million compound screen. It is expected that slight deviations from a crystal structure may be helpful for virtual screens as a crystal structure is a conformational snapshot, however it is unknown how much deviation is acceptable before prospective screens fail. Further, selecting models based on their ability to successfully dock known agonists or antagonists could enable prospective virtual screens to not only identify hits but identify hits with a given activity. Lastly, the ability to automate much of the process would enable wide-scale application of the method to the large number of understudied GPCRs and other proteins considered part of the dark genome. An important, immediate target for application of these methods are in the identification of non-opioid receptor targeting therapies for pain management. A family of GPCRs that may regulate pain pathways are the RF-amide peptide receptors. Importantly, we have found that the pyroglutamylated RF-amide peptide receptor (QRFPR) is upregulated in sensory neurons in a mouse model of chronic pain. Unfortunately, selective, small molecules have not been made publicly available for QRFPR or other RF-amide receptors. The identification of tool compounds for these receptors would allow researchers to effectively study the contributions of each receptor in the biology of pain. Further, success in this application would provide more confidence in a systematic application of the method towards the remaining understudied GPCRs.
Despite G-protein coupled receptors (GPCRs) being the most heavily targeted class of proteins by drugs, over 100 GPCRs remain understudied and lack small molecules that can selectively activate or inactivate the receptor. The development of methods that can identify tool compounds for these understudied GPCRs will allow scientists to conduct basic research on these proteins and lead towards novel drug development. One field where this method has potential for significant impact is in the development of non-opioid receptor-based pain therapies, specifically for the pyroglutamylated RF-amide peptide receptor (QRFPR).
