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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 （Cannabinoid receptor subtype 2, CB2）是一种a类家族G蛋白偶联受体（GPCR），主要位于

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.

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

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