Xiang-Qun Xie

Beta-caryophyllene is a dietary cannabinoid.

The psychoactive cannabinoids from Cannabis sativa L. and the arachidonic acid-derived endocannabinoids are nonselective natural ligands for cannabinoid receptor type 1 (CB1) and CB2 receptors. Although the CB1 receptor is responsible for the psychomodulatory effects, activation of the CB2 receptor is a potential therapeutic strategy for the treatment of inflammation, pain, atherosclerosis, and osteoporosis. Here, we report that the widespread plant volatile (E)-β-caryophyllene [(E)-BCP] selectively binds to the CB2 receptor (Ki = 155 ± 4 nM) and that it is a functional CB2 agonist. Intriguingly, (E)-BCP is a common constituent of the essential oils of numerous spice and food plants and a major component in Cannabis. Molecular docking simulations have identified a putative binding site of (E)-BCP in the CB2 receptor, showing ligand π–π stacking interactions with residues F117 and W258. Upon binding to the CB2 receptor, (E)-BCP inhibits adenylate cylcase, leads to intracellular calcium transients and weakly activates the mitogen-activated kinases Erk1/2 and p38 in primary human monocytes. (E)-BCP (500 nM) inhibits lipopolysaccharide (LPS)-induced proinflammatory cytokine expression in peripheral blood and attenuates LPS-stimulated Erk1/2 and JNK1/2 phosphorylation in monocytes. Furthermore, peroral (E)-BCP at 5 mg/kg strongly reduces the carrageenan-induced inflammatory response in wild-type mice but not in mice lacking CB2 receptors, providing evidence that this natural product exerts cannabimimetic effects in vivo. These results identify (E)-BCP as a functional nonpsychoactive CB2 receptor ligand in foodstuff and as a macrocyclic antiinflammatory cannabinoid in Cannabis.

Alkylamides from Echinacea Are a New Class of Cannabinomimetics CANNABINOID TYPE 2 RECEPTOR-DEPENDENT AND -INDEPENDENT IMMUNOMODULATORY EFFECTS

Alkylamides (alkamides) from Echinacea modulate tumor necrosis factor α mRNA expression in human monocytes/macrophages via the cannabinoid type 2 (CB2) receptor (Gertsch, J., Schoop, R., Kuenzle, U., and Suter, A. (2004) FEBS Lett. 577, 563–569). Here we show that the alkylamides dodeca-2E,4E,8Z,10Z-tetraenoic acid isobutylamide (A1) and dodeca-2E,4E-dienoic acid isobutylamide (A2) bind to the CB2 receptor more strongly than the endogenous cannabinoids. The Ki values of A1 and A2 (CB2 ∼60 nm;CB1 >1500 nm) were determined by displacement of the synthetic high affinity cannabinoid ligand [3H]CP-55,940. Molecular modeling suggests that alkylamides bind in the solvent-accessible cavity in CB2, directed by H-bonding and π -π interactions. In a screen with 49 other pharmacologically relevant receptors, it could be shown that A1 and A2 specifically bind to CB2 and CB1. A1 and A2 elevated total intracellular Ca2+ in CB2-positive but not in CB2-negative promyelocytic HL60 cells, an effect that was inhibited by the CB2 antagonist SR144528. At 50 nm, A1, A2, and the endogenous cannabinoid anandamide (CB2 Ki >200 nm) up-regulated constitutive interleukin (IL)-6 expression in human whole blood in a seemingly CB2-dependent manner. A1, A2, anandamide, the CB2 antagonist SR144528 (Ki <10 nm), and also the non-CB2-binding alkylamide undeca-2E-ene,8,10-diynoic acid isobutylamide all significantly inhibited lipopolysaccharide-induced tumor necrosis factor α, IL-1β, and IL-12p70 expression (5–500 nm) in a CB2-independent manner. Alkylamides and anandamide also showed weak differential effects on anti-CD3-versus anti-CD28-stimulated cytokine expression in human whole blood. Overall, alkylamides, anandamide, and SR144528 potently inhibited lipopolysaccharide-induced inflammation in human whole blood and exerted modulatory effects on cytokine expression, but these effects are not exclusively related to CB2 binding.

GPCR structure-based virtual screening approach for CB2 antagonist search.

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database

Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand–target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.

Data Mining a Small Molecule Drug Screening Representative Subset from NIH PubChem

PubChem is a scientific showcase of the NIH Roadmap Initiatives. It is a compound repository created to facilitate information exchange and data sharing among the NIH Roadmap-funded Molecular Library Screening Center Network (MLSCN) and the scientific community. However, PubChem has more than 10 million records of compound information. It will be challenging to conduct a drug screening of the whole database of millions of compounds. Thus, the purpose of the present study was to develop a data mining cheminformatics approach in order to construct a representative and structure-diverse sublibrary from the large PubChem database. In this study, a new chemical diverse representative subset, rePubChem, was selected by whole-molecule chemistry-space matrix calculation using the cell-based partition algorithm. The representative subset was generated and was then subjected to evaluations by compound property analyses based on 1D and 2D molecular descriptors. The new subset was also examined and assessed for self-similarity analysis based on 2D molecular fingerprints in comparing with the source compound library. The new subset has a much smaller library size (540K compounds) with minimum similarity and redundancy without loss of the structural diversity and basic molecular properties of its parent library (5.3 million compounds). The new representative subset library generated could be a valuable structure-diverse compound resource for in silico virtual screening and in vitro HTS drug screening. In addition, the established subset generation method of using the combined cell-based chemistry-space partition metrics with pairwised 2D fingerprint-based similarity search approaches will also be important to a broad scientific community interested in acquiring structurally diverse compounds for efficient drug screening, building representative virtual combinatorial chemistry libraries for syntheses, and data mining large compound databases like the PubChem library in general.

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion

In this work, a genetic algorithm (GA) was applied to build up a set of QSPR (quantitative structure-property relationship) models for human absolute oral bioavailability, plasma protein binding, and urinary excretion using the counts of molecular fragments as descriptors. For a pharmacokinetic property, the consensus score of a set of models (20 or 30) was found to improve the correlation coefficient and reduce the standard error significantly. Key fragments that may boost or reduce pharmacokinetic properties were also identified. Databases searches were performed for a set of key fragments identified by bioavailability models. The percentage of hit rates of bioavailability-boosting fragments were significantly higher than those of bioavailability-reducing fragments for MDDR (MDL Drug Data Report), a database of drugs and drug leads entered or entering development. On the other hand, the opposite trend was observed for ACD (Available Chemicals Directory), a database of all kinds of available compounds.

Modeling, molecular dynamics simulation, and mutation validation for structure of cannabinoid receptor 2 based on known crystal structures of GPCRs.

The cannabinoid receptor 2 (CB2) plays an important role in the immune system. Although a few of GPCRs crystallographic structures have been reported, it is still challenging to obtain functional transmembrane proteins and high resolution X-ray crystal structures, such as for the CB2 receptor. In the present work, we used 10 reported crystal structures of GPCRs which had high sequence identities with CB2 to construct homology-based comparative CB2 models. We applied these 10 models to perform a prescreen by using a training set consisting of 20 CB2 active compounds and 980 compounds randomly selected from the National Cancer Institute (NCI) database. We then utilized the known 170 cannabinoid receptor 1 (CB1) or CB2 selective compounds for further validation. Based on the docking results, we selected one CB2 model (constructed by β1AR) that was most consistent with the known experimental data, revealing that the defined binding pocket in our CB2 model was well-correlated with the training and testing data studies. Importantly, we identified a potential allosteric binding pocket adjacent to the orthosteric ligand-binding site, which is similar to the reported allosteric pocket for sodium ion Na+ in the A2AAR and the δ-opioid receptor. Our studies in correlation of our data with others suggested that sodium may reduce the binding affinities of endogenous agonists or its analogs to CB2. We performed a series of docking studies to compare the important residues in the binding pockets of CB2 with CB1, including antagonist, agonist, and our CB2 neutral compound (neutral antagonist) XIE35-1001. Then, we carried out 50 ns molecular dynamics (MD) simulations for the CB2 docked with SR144528 and CP55940, respectively. We found that the conformational changes of CB2 upon antagonist/agonist binding were congruent with recent reports of those for other GPCRs. Based on these results, we further examined one known residue, Val1133.32, and predicted two new residues, Phe183 in ECL2 and Phe2817.35, that were important for SR144528 and CP55940 binding to CB2. We then performed site-directed mutation experimental study for these residues and validated the predictions by radiometric binding affinity assay.

AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research.

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation. Overall, AlzPlatform will enrich our knowledge for AD target identification, drug discovery, and polypharmacology analyses and, also, facilitate the chemogenomics data sharing and information exchange/communications in aid of new anti-AD drug discovery and development.

Nanoassembly of surfactants with interfacial drug-interactive motifs as tailor-designed drug carriers.

PEGylated lipopeptide surfactants carrying drug-interactive motifs specific for a peptide-nitroxide antioxidant, JP4-039, were designed and constructed to facilitate the solubilization of this drug candidate as micelles and emulsion nanoparticles. A simple screening process based on the ability that prevents the formation of crystals of JP4-039 in aqueous solution was used to identify agents that have potential drug-interactive activities. Several protected lysine derivatives possessing this activity were identified, of which α-Fmoc-ε-t-Boc lysine is the most potent, followed by α-Cbz- and α-iso-butyloxycarbonyl-ε-t-Boc-lysine. Using a polymer-supported liquid-phase synthesis approach, a series of synthetic lipopeptide surfactants with PEG headgroup, varied numbers and geometries of α-Fmoc or α-Cbz-lysyl groups located at interfacial region as the drug-interactive domains, and oleoyl chains as the hydrophobic tails were synthesized. All α-Fmoc-lysyl-containing lipopeptide surfactants were able to solubilize JP4-039 as micelles, with enhanced solubilizing activity for surfactants with increased numbers of α-Fmoc groups. The PEGylated lipopeptide surfactants with α-Fmoc-lysyl groups alone tend to form filamentous or wormlike micelles. The presence of JP4-039 transformed α-Fmoc-containing filamentous micelles into dots and barlike mixed micelles with substantially reduced sizes. Fluorescence quenching and NMR studies revealed that the drug and surfactant molecules were in close proximity in the complex. JP4-039-loaded emulsion carrying α-Cbz-containing surfactants demonstrated enhanced stability over drug-loaded emulsion without lipopeptide surfactants. JP4-039 emulsion showed a significant mitigation effect on mice exposed to a lethal dose of radiation. PEGylated lipopeptides with an interfacially located drug-interactive domain are therefore tailor-designed formulation materials potentially useful for drug development.