Cannabinoid CB2 Receptor Structure and Allosteric Modulators

大麻素 CB2 受体结构和变构调节剂

• 批准号: 10612431
• 负责人: Xiang-Qun Xie
• 金额: $ 63.31万
• 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/10612431
• 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; Cyclic AMP; Databases; Detergents; Development; Disease; Docking; Drug Targeting; Exhibits; Family; G-Protein-Coupled Receptors; GTP-Binding Proteins; Genomics; 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; 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A类家族G蛋白偶联受体（GPCR），主要位于 在免疫相关组织中，但也在大脑的特定区域，并涉及几种炎症 疾病和成瘾。靶向CB2的药物是慢性神经性疼痛的有吸引力的治疗选择 和神经炎性自身免疫性疾病，因为它们避免了有害的精神作用， 与CB1有关。虽然药物开发工作主要集中在小分子靶向 正构位点、选择性差的局限性、缺乏效力和抗性的发展阻碍了 这样的努力。目前，在鉴定GPCR变构调节剂方面存在很大的兴趣，所述GPCR变构调节剂或者增强GPCR的活性，或者增强GPCR的活性。 （正向别构调节剂或PAM）或抑制（负向别构调节剂或NAM）激动剂诱导的细胞内信号传导。 受体活性PAM/NAM通常表现出改善的亚型选择性和时空敏感性，以及 与正构配体相比，潜在的偏向信号传导性质。我们最近报道了一个3.2 μ m的冷冻电镜 激动剂结合的人CB2-Gi复合物的结构。基于这一进展，本提案的总体目标 是获得CB2变构调节的结构理解，并使用我们的综合计算和 实验药物化学/生物学方法来设计和合成新的变构调节剂， 开发具有治疗CB2相关疾病潜力的CB2特异性小分子。因此，我们首先建议 通过冷冻电镜和X射线晶体学阐明CB2变构调节剂作用的结构基础 接近。为了实现这一目标，我们将推进我们建立的CB2结构研究方法，以获得 CB2与已知PAM或NAM的结构。随后，我们计划进行新型CB2变构剂的计算机设计， 通过我们建立的分子指纹机器学习（ML）计算算法和受体 对接方法，基于我们报告的化学基因组学大麻素分子信息数据库（CBID） 和3D CB2-Gi cryo-EM结构;将使用我们的基于片段的 设计（FBD）方法和我们建立的ML分类器和特征排序将被应用于虚拟选择 点击率了通过调整结构信息，将结果与基于CB2结构的调制器设计相关联 从我们最近的CB2-Gi cryo-EM结构和我们的新型分子复合物表征系统（MCCS）中获得 算法最后，对CB2、PAM和NAM配体进行药物化学合成，并进行验证 通过放射性结合和细胞功能测定。我们最近发现了一种可能的 CB2 NAM，这些目标的成功完成将提供关于CB2变构的前所未有的结构信息。 口袋，确定有前途的新的CB2变构调节剂，并帮助阐明CB2信号和药理学。

项目成果

Differential performance of RoseTTAFold in antibody modeling.

RoseTTAFold 在抗体建模中的差异化性能。

• DOI: 10.1093/bib/bbac152
• 发表时间: 2022
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5
• 作者: Liang,Tianjian;Jiang,Chen;Yuan,Jiayi;Othman,Yasmin;Xie,Xiang-Qun;Feng,Zhiwei
• 通讯作者: Feng,Zhiwei

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries.

• DOI: 10.3390/cells11050915
• 发表时间: 2022-03-07
• 期刊: Cells
• 影响因子: 6
• 作者: Bian Y;Xie XQ
• 通讯作者: Xie XQ

How Do Modulators Affect the Orthosteric and Allosteric Binding Pockets?

• DOI: 10.1021/acschemneuro.1c00749
• 发表时间: 2022-04-06
• 期刊: ACS CHEMICAL NEUROSCIENCE
• 影响因子: 5
• 作者: Chen, Chih-Jung;Jiang, Chen;Yuan, Jiayi;Chen, Maozi;Cuyler, Jacob;Xie, Xiang-Qun;Feng, Zhiwei
• 通讯作者: Feng, Zhiwei

Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence.

• DOI: 10.3390/biom11060870
• 发表时间: 2021-06-11
• 期刊: Biomolecules
• 影响因子: 5.5
• 作者: Hou T;Bian Y;McGuire T;Xie XQ
• 通讯作者: Xie XQ

Discovery of a pyrano[2,3-b]pyridine derivative YX-2102 as a cannabinoid receptor 2 agonist for alleviating lung fibrosis.

发现pyrano [2,3-B]吡啶衍生物YX-2102作为大麻素受体2激动剂，用于减轻肺纤维化。

• DOI: 10.1186/s12967-022-03773-1
• 发表时间: 2022-12-06
• 期刊: JOURNAL OF TRANSLATIONAL MEDICINE
• 影响因子: 7.4
• 作者: Liu, Tao;Gu, Jing;Yuan, Yi;Yang, Qunfang;Zheng, Peng-Fei;Shan, Changyu;Wang, Fangqin;Li, Hongwei;Xie, Xiang-Qun;Chen, Xiao-Hong;Ouyang, Qin
• 通讯作者: Ouyang, Qin