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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

基本信息

批准号：
10084705
负责人：
Brian J Bender
金额：
$ 6.64万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10084705
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

项目摘要

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.

项目总结/摘要基于结构的发现是一种经过验证的方法，用于鉴定对药物具有有效性和选择性的新化合物。一个特定的药物靶点然而，由于这种方法依赖于药物靶点结构的知识，我们受到限制通过已知结构的可用性。G蛋白偶联受体（GPCR）是最重要的靶向家族然而，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有限公司为了利用基于结构的发现来研究GPCR，我们必须使用同源性模型虚拟屏幕。当使用同源性模型进行前瞻性筛选时，有成功的例子，但有很多案例都失败了，原因不明。为了制定一套规则，为了有效地指导同源模型在虚拟屏幕中的使用，我们必须分析建模的每个输入，系统对接过程。重要的未知数包括同源性模型的准确性，给定的晶体结构，已知活性物质在数量和活性方面的作用，可用于区分有效的模型，以及在4亿个复合筛选后自动选择命中的能力。预计在与晶体结构的轻微偏差对于虚拟屏幕可能是有帮助的，构象快照，但在前瞻性筛选之前，不知道多少偏差是可接受的失败此外，基于其成功对接已知激动剂或拮抗剂的能力来选择模型可使预期的虚拟屏幕不仅能够识别命中，而且能够识别具有给定活动的命中。最后，能力使大部分过程自动化将使该方法能够广泛应用于大量的 GPCR和其他被认为是暗基因组一部分的蛋白质研究不足。一个重要的，直接的目标，这些方法的应用是在确定非阿片受体靶向治疗疼痛管理可以调节疼痛通路的GPCR家族是RF-酰胺肽受体。重要的是，我们已经发现焦谷氨酰化RF-酰胺肽受体（QRFPR）在细胞中上调，慢性疼痛小鼠模型中的感觉神经元。不幸的是，选择性的小分子还没有被对于QRFPR或其他RF-酰胺受体公开可用。这些工具化合物的鉴定受体的研究将使研究人员能够有效地研究每种受体在疼痛生物学中的作用。此外，这一应用的成功将为系统地应用该方法提供更大的信心剩下的未充分研究的GPCR。