Cannabinoid CB2 Receptor Structure and Allosteric Modulators

大麻素 CB2 受体结构和变构调节剂

• 批准号: 10448397
• 负责人: Xiang-Qun Xie
• 金额: $ 63.16万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10448397
• 关键词: 3-Dimensional; Affinity; Agonist; Algorithms; Allosteric Site; Antibodies; Autoimmune Diseases; Binding; Binding Sites; Biological Assay; Biology; Brain region; CNR1 gene; CNR2 gene; Cannabinoids; Chemicals; Chronic; Complex; Cryoelectron Microscopy; Crystallization; Cyclic AMP; Databases; Detergents; Development; Disease; Docking; Drug Targeting; Exhibits; Family; G-Protein-Coupled Receptors; GTP-Binding Proteins; Goals; Heterogeneity; Human; Hydrophobicity; Immune; In Vitro; Inflammatory; Joints; Legal patent; Libraries; Ligand Binding; Ligands; Machine Learning; Malignant Neoplasms; Measures; Membrane Proteins; Methods; Modeling; Molecular; Molecular Conformation; Molecular Profiling; Mutation; Neurologic; Osteoporosis; Outcome; Pain; Pharmaceutical Chemistry; Pharmacology; Pilot Projects; Property; Publications; Publishing; Reporting; Research; Resistance development; Roentgen Rays; Role; Signal Transduction; Site; Site-Directed Mutagenesis; Structure; Synthesis Chemistry; System; Techniques; Technology; Therapeutic; Tissues; Validation; Work; X-Ray Crystallography; addiction; alternative treatment; antagonist; base; cannabinoid receptor; cheminformatics; computational chemistry; deep learning; design; drug development; drug discovery; endogenous cannabinoid system; improved; in silico; in vivo; innovation; insight; interest; learning classifier; machine learning classifier; neuroinflammation; novel; pharmacophore; positive allosteric modulator; pressure; prevent; prospective; prototype; receptor; screening; small molecule; small molecule libraries; spatiotemporal; structural biology; synthetic cannabinoid; targeted treatment; therapeutic target; three dimensional structure; tool; virtual

Cannabinoid receptor subtype 2 (CB2) is a class-A family G protein-coupled receptor (GPCR), located primarily in immune-associated tissues but also in specific regions of the brain, and implicated in several inflammatory diseases and addiction. Drugs targeting CB2 are attractive treatment alternatives for chronic neurological pain and neuroinflammatory autoimmune diseases since they avoid deleterious psychotropic effects that are associated with CB1. While drug development efforts have been primarily focused on small molecules targeting the orthosteric site, limitations of poor selectivity, lack of efficacy, and development of resistance have hampered such effort. At present, there is great interests in identifying GPCR allosteric modulators that either enhance (positive allosteric modulators, or PAMs) or inhibit (negative allosteric modulators, or NAMs) agonist-induced receptor activity. PAMs/NAMs often exhibit improved subtype selectivity and spatiotemporal sensitivity, as well as potential biased signaling properties compared to orthosteric ligands. We have recently reported a 3.2 Å cryo-EM structure of the agonist-bound human CB2-Gi complex. Based on such progress, the overall goals of this proposal are to obtain a structural understanding of CB2 allosteric modulation and use our integrated computational and experimental medicinal chemistry/biology approaches to design and synthesize novel allosteric modulators for the development of CB2-specific small-molecules with potential to treat CB2-associated maladies. Thus, we first propose to elucidate the structural basis for the action of CB2 allosteric modulators by cryo-EM and X-ray crystallography approaches. To achieve the goal, we will advance our established methods for structural studies on CB2 to obtain structure of CB2 with known PAMs or NAMs. Subsequently, we plan to perform in silico design of novel CB2 allosteric modulators by our established molecular fingerprint machine-learning (ML) computing algorithms and receptor docking approaches, on basis of our reported chemogenomics cannabinoid molecular information database (CBID) and 3D CB2-Gi cryo-EM structure; a virtual allosteric modulator library will be constructed using our fragment-based design (FBD) method and our established ML-classifiers and features-ranking will be applied for selection of virtual hits. Results will be correlated with CB2 structure-based modulator design via adapting the structural information obtained from our recent CB2-Gi cryo-EM structure and our novel molecular complex characterizing system (MCCS) algorithm. Finally, we will carry out medicinal chemistry synthesis of CB2 PAM and NAM ligands and validate them by radiometric binding and cellular functional assays. With the proof-of-evidence of our recent discovery of a putative CB2 NAM, successful completion of these Aims will provide unprecedented structural information on CB2 allosteric pockets, identify promising new CB2 allosteric modulators, and help to elucidate CB2 signaling and pharmacology.

大麻素受体亚型2(CB2)是一种A类G蛋白偶联受体，主要定位于 在免疫相关组织中，也在大脑的特定区域，并牵涉到几个炎症 疾病和毒瘾。靶向CB2的药物是治疗慢性神经性疼痛的有吸引力的替代药物 和神经炎性自身免疫性疾病，因为它们避免了有害的精神药物效应 与CB1关联。虽然药物开发工作主要集中在小分子靶向上 正构体位置、选择性差的局限性、缺乏效力以及耐药性的发展都受到了阻碍 这样的努力。目前，人们对鉴定GPCR变构调节剂非常感兴趣，这些调节剂既可以增强 (正变构调节剂或PAM)或抑制(负变构调节剂或NAM)激动剂诱导的 感受器活性。PAMS/NAMS往往表现出更好的亚型选择性和时空敏感性，以及 与正构体配体相比，潜在的偏向信号特性。我们最近报告了一种3.2？低温-EM 激动剂结合的人CB2-GI复合体的结构。基于这样的进展，这项提案的总体目标 是为了获得对CB2变构调制的结构理解，并使用我们集成的计算和 设计和合成新型变构调节剂的实验药物化学/生物学方法 具有治疗CB2相关疾病潜力的CB2特异性小分子的开发。因此，我们首先提出 用低温电子显微镜和X射线结晶学阐明CB2变构调节剂作用的结构基础 接近了。为了实现这一目标，我们将推进现有的CB2结构研究方法，以获得 具有已知PAM或NAM的CB2的结构。随后，我们计划进行新型cb2变构的电子设计。 调制器由我们建立的分子指纹机器学习(ML)计算算法和接收器 基于我们已报道的化学基因组学大麻素分子信息数据库(CBID)的对接方法 和3D CB2-GI冷冻-EM结构；利用我们的片段构建了一个虚拟变构调制器文库 设计(FBD)方法和我们建立的最大似然分类器和特征排序将应用于虚拟的选择 点击率。通过对结构信息的适应，将结果与基于CB2结构的调制器设计相关联 从我们最新的CB2-GI低温EM结构和我们新的分子络合物表征系统(MCCS)中获得 算法。最后，我们将进行CB2PAM和NAM配体的药物化学合成并进行验证 通过放射结合和细胞功能分析。我们最近发现了一种推定的 CB2 NAM，成功完成这些目标将提供前所未有的关于CB2变构的结构信息 口袋，确定有希望的新的CB2变构调节剂，并帮助阐明CB2信号和药理学。