ABSTRACT
Drugs selectively targeting CB2 hold promise for treating neurodegenerative disorders, inflammation, and pain while avoiding psychotropic side effects mediated by CB1. The mechanisms underlying CB2 activation and signaling are poorly understood but critical for drug design. Here we report the cryo-EM structure of the human CB2-Gi signaling complex bound to the agonist WIN 55,212-2. The 3D structure reveals the binding mode of WIN 55,212-2 and structural determinants for distinguishing CB2 agonists from antagonists, which are supported by a pair of rationally designed agonist and antagonist. Further structural analyses with computational docking results uncover the differences between CB2 and CB1 in receptor activation, ligand recognition, and Gi coupling. These findings are expected to facilitate rational structure-based discovery of drugs targeting the cannabinoid system.
Subject(s)
GTP-Binding Protein alpha Subunits, Gi-Go/chemistry , Receptor, Cannabinoid, CB2/chemistry , Signal Transduction , Animals , Binding Sites , CHO Cells , Cannabinoid Receptor Agonists/chemical synthesis , Cannabinoid Receptor Agonists/pharmacology , Cannabinoid Receptor Antagonists/chemical synthesis , Cannabinoid Receptor Antagonists/pharmacology , Cricetinae , Cricetulus , Cryoelectron Microscopy , GTP-Binding Protein alpha Subunits, Gi-Go/metabolism , Humans , Molecular Docking Simulation , Protein Binding , Receptor, Cannabinoid, CB2/agonists , Receptor, Cannabinoid, CB2/antagonists & inhibitors , Receptor, Cannabinoid, CB2/metabolism , Sf9 Cells , SpodopteraABSTRACT
Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.
Subject(s)
Brain Injuries, Traumatic/genetics , Drug Discovery/methods , Gene Expression Profiling/methods , Pharmacogenomic Testing/methods , Animals , Computer Simulation , Humans , TranscriptomeABSTRACT
Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action. To facilitate stem cell related small molecule study and provide a better understanding of the associated signaling pathways, we have constructed a comprehensive domain-specific chemogenomics resource, called StemCellCKB ( http://www.cbligand.org/StemCellCKB/ ). This new cloud-computing platform describes the chemical molecules, genes, proteins, and signaling pathways implicated in stem cell regulation. StemCellCKB is also implemented with web applications designed specifically to aid in the identification of stem cell relevant protein targets, including TargetHunter, a machine-learning algorithm for predicting small molecule targets based on molecular fingerprints, and HTDocking, a high-throughput docking module for target prediction and systems-pharmacology analyses. We have systematically tested StemCellCKB to verify data integrity. Target-prediction accuracy has also been validated against the reported known target/compound associations. This proof-of-concept example demonstrates that StemCellCKB can (1) accurately predict the macromolecular targets of existing stem cell modulators and (2) identify novel small molecules capable of probing stem cell signaling mechanisms, for use in systems-pharmacology studies. StemCellCKB facilitates the exploration and exchange of stem cell chemogenomics data among members of the broader research community.
