Advancing Homology Models to Identify Compounds for Understudied GPCRs

推进同源模型来识别待研究 GPCR 的化合物

• 批准号: 9909701
• 负责人: Brian J Bender
• 金额: $ 6.49万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9909701
• 关键词: Afferent Neurons; Agonist; Amides; Basic Science; Biology; Collaborations; Consensus; Crystallization; Development; Disease; Docking; Drug Targeting; Failure; Family; Functional disorder; G-Protein-Coupled Receptors; GPR15 gene; Genes; Genome; Goals; Homology Modeling; Knowledge; Lead; Letters; Libraries; Ligands; Link; Measures; Membrane Proteins; Methods; Modeling; Molecular Conformation; Mus; Mutagenesis; Nature; Pain; Pain management; Pathway interactions; Peptide Receptor; Peptides; Performance; Pharmaceutical Preparations; Process; Protein Family; Proteins; Research; Research Personnel; Risk; Role; Scientist; Source; Structure; Testing; Training; Up-Regulation; base; career; chronic pain; drug development; drug discovery; hypocretin; improved; in silico; in vitro Assay; method development; mouse model; non-opioid analgesic; novel; novel therapeutics; pain model; predictive modeling; predictive test; programs; prospective; receptor; screening; small molecule; success; targeted treatment; tool; virtual; virtual model

Project Summary/Abstract 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.

项目摘要/摘要 基于结构的发现是一种识别潜在的新型化合物的经过验证的方法 给定的药物靶标。但是，由于这种方法依赖于对药物靶标的结构的了解，因此我们受到限制 根据已知结构的可用性。 G蛋白偶联受体（GPCR）是最靶向的家族 蛋白质的蛋白质，但由于它们作为膜蛋白的性质，我们对这些蛋白质的结构知识是 有限的。为了利用基于结构的发现来了解GPCR，我们必须使用同源模型 对于虚拟屏幕。在使用同源模型进行预期屏幕时，有许多成功的例子，但是 有很多情况失败，原因未知。为了制定一套可以更多的规则 有效指导在虚拟屏幕中使用同源模型的使用，我们必须分析每个输入到建模和 系统地对接过程。重要的未知数包括同源模型相对于 给定的晶体结构，无论是在数量还是活动中的已知活性物的作用，可用于区分 有效的型号，以及在4亿个复合屏幕之后自动击球的能力。预计 与晶体结构略有不同可能对虚拟屏幕有帮助，因为晶体结构是 构象快照，但是未知在预期屏幕之前可接受多少出发 失败。此外，选择模型基于成功码头已知激动剂或拮抗剂的能力可以 使前瞻性虚拟屏幕不仅可以识别命令，还可以识别给定活动的命运。最后，能力 为了使大部分过程自动化将使该方法大规模应用到大量 研究的GPCR和其他蛋白质被认为是黑暗基因组的一部分。一个重要的直接目标 这些方法的应用是鉴定非阿片类受体靶向疼痛疗法 管理。可能调节疼痛途径的GPCR家族是RF酰胺肽受体。 重要的是，我们发现焦谷氨酸化的RF-酰胺肽受体（QRFPR）已更新 慢性疼痛的小鼠模型中的感觉神经元。不幸的是，选择性，小分子尚未 公开可用于QRFPR或其他RF酰胺受体。这些工具化合物的识别 受体将使研究人员能够有效研究每个受体在疼痛生物学中的贡献。 此外，该应用程序的成功将对该方法的系统应用提供更多信心 走向剩余的理解GPCR。