Cannabinoid CB2 Receptor Structure and Allosteric Modulators

大麻素 CB2 受体结构和变构调节剂

基本信息

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
项目类别：
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

项目摘要

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G蛋白偶联受体（GPCR），位于主要在免疫相关的组织中，但在大脑的特定区域也暗示了几种炎症疾病和成瘾。靶向CB2的药物是慢性神经系统疼痛的有吸引力的治疗方法和神经炎症性自身免疫性疾病，因为它们避免了有害的精神作用与CB1相关。尽管药物开发工作主要集中在针对的小分子上正常位置，选择性差的局限性，缺乏效率和阻力的发展受到了阻碍这样的努力。目前，识别GPCR变构调节剂的兴趣很大（阳性变构调节剂，或PAM）或抑制（负变构调节剂或NAMS）激动剂诱导的受体活性。 PAM/NAM通常表现出改善的亚型选择性和时空灵敏度，以及与正常配体相比，潜在的偏置信号传导特性。我们最近报道了3.2Å的冷冻EM 激动剂结合的人CB2-GI复合物的结构。基于这样的进展，该提案的总体目标要获得对CB2变构调制的结构理解，并使用我们的集成计算和实验性医学化学/生物学方法设计和合成新型变构调节剂 CB2特异性小分子的发展具有治疗CB2相关疾病的潜力。那，我们首先提出阐明Cero-EM和X射线晶体学的CB2变构调节剂作用的结构基础方法。为了实现目标，我们将推进对CB2结构研究的既定方法具有已知PAM或NAM的CB2结构。随后，我们计划在新型CB2变构中执行调节剂通过我们既定的分子指纹机器学习（ML）计算算法和接收器对接方法，根据我们报道的化学基因组学大麻素分子信息数据库（CBID）和3D CB2-GI冷冻EM结构；将使用我们的基于片段的虚拟变构调制器库构建设计（FBD）方法和我们已建立的ML分类符和功能级别将用于选择虚拟命中。结果将与基于CB2结构的调制器设计相关，通过调整结构信息从我们最近的CB2-GI冷冻EM结构和我们的新分子复合体特征系统（MCC）获得算法。最后，我们将执行CB2 PAM和NAM配体的医学化学合成并验证它们通过辐射结合和细胞功能测定。随着我们最近发现的推定的证据证明 CB2 NAM，这些目标的成功完成将为CB2变构提供前所未有的结构信息口袋，确定有希望的新CB2变构调节剂，并有助于阐明CB2信号传导和药理学。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

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

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

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