GPCR structure-based virtual screening approach for CB2 antagonist search

Structural and functional insights into the G protein-coupled receptors: CB1 and CB2

The cannabinoid receptors CB1 and CB2 mediate a variety of physiological processes and continue to be explored as desirable drug targets. Both receptors are activated by the endogenous endocannabinoids and the psychoactive components of marijuana. Over the years, many efforts have been made to make selective ligands; however, the high degree of homology between cannabinoid receptor subtypes introduces challenges in studying either receptor in isolation. Recent advancements in structure biology have resulted in a surge of high-resolution structures, enriching our knowledge and understanding of receptor structure and function. In this review, of recent cannabinoid receptor structures, key features of the inactive and active state CB1 and CB2 are presented. These structures will provide additional insight into the modulation and signaling mechanism of cannabinoid receptors CB1 and CB2 and aid in the development of future therapeutics.

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy

The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pK_i of 5.15–6.66, among which five compounds showed antagonistic activities with pIC₅₀ of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pK_i of 6.66 and pIC₅₀ of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.

Virtual Screening of Chinese Medicine Small Molecule Compounds Targeting SARS-CoV-2 3CL Protease (3CL pro)

Background:

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has attracted worldwide attention due to its high infectivity and pathogenicity.

Objective:

The purpose of this study is to develop drugs with therapeutic potentials for COVID-19.

Methods:

we selected the crystal structure of 3CL pro to perform virtual screening against natural products in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Then, molecular dynamics (MD) simulation was carried out to explore the binding mode between compounds and 3CL pro.

Results and Discussion:

A total of 6 candidates with good theoretical binding affinity to 3CL pro were identified. The binding mode after MD shows that hydrogen bonding and hydrophobic interaction play an important role in the binding process. Finally, based on the free binding energy analysis, the candidate natural product Gypenoside LXXV may bind to 3CL pro with high binding affinity.

Conclusion:

The natural product Gypenoside LXXV may have good potential anti-SARS-COV-2 activity.

β-Adrenergic receptor, an essential target in cardiovascular diseases

β-Adrenergic receptors (βARs) belong to a large family of cell surface receptors known as G protein-coupled receptors (GPCRs). They are coupled to Gs protein (Gαs) for the activation of adenylyl cyclase (AC) yielding cyclic AMP (_CAMP), and this provides valuable responses, which can affect the cardiac function such as injury. The binding of an agonist to βAR enhances conformation changes that lead to the Gαs subtype of heterotrimeric G protein which is the AC stimulatory G protein for activation of _CAMP in the cells. However, cardiovascular diseases (CVD) have been reported as having an increased rate of death and β1AR, and β2AR are a promising tool that improves the regulatory function in the cardiovascular system (CVS) via signaling. It increases the Gα level, which activates βAR kinase (βARK) that affects and enhances the progression of heart failure (HF) through the activation of cardiomyocyte βARs. We also explained that an increase in GPCR kinases (GRKs) would practically improve the HF pathogenesis and this occurs via the desensitization of βARs, which causes the loss of contractile reserve. The consistency or overstimulation of catecholamines contributes to CVD such as stroke, HF, and cardiac hypertrophy. When there is a decrease in catecholamine responsiveness, it causes aging in old people because the reduction of βAR sensitivity and density in the myocardium enhances downregulation of βARs to AC in the human heart.

Structure-Based Design of Novel Biphenyl Amide Antagonists of Human Transient Receptor Potential Cation Channel Subfamily M Member 8 Channels with Potential Implications in the Treatment of Sensory Neuropathies

Structure-activity relationship studies of a reported menthol-based transient receptor potential cation channel subfamily M member 8 channel (TRPM8) antagonist, guided by computational simulations and structure-based design, uncovers a novel series of TRPM8 antagonists with >10-fold selectivity versus related TRP subtypes. Spiro[4.5]decan-8-yl analogue 14 inhibits icilin-evoked Ca²⁺ entry in HEK-293 cells stably expressing human TRPM8 (hTRPM8) with an IC₅₀ of 2.4 ± 1.0 nM, while in whole-cell patch-clamp recordings this analogue inhibits menthol-evoked currents with a hTRPM8 IC₅₀ of 64 ± 2 nM. Molecular dynamics (MD) simulations of compound 14 in our homology model of hTRPM8 suggest that this antagonist forms extensive hydrophobic contacts within the orthosteric site. In the wet dog shakes (WDS) assay, compound 14 dose-dependently blocks icilin-triggered shaking behaviors in mice. Upon local administration, compound 14 dose dependently inhibits cold allodynia evoked by the chemotherapy oxaliplatin in a murine model of peripheral neuropathy at microgram doses. Our findings suggest that 14 and other biphenyl amide analogues within our series can find utility as potent antagonist chemical probes derived from (-)-menthol as well as small molecule therapeutic scaffolds for chemotherapy-induced peripheral neuropathy (CIPN) and other sensory neuropathies.

Artificial intelligence and big data facilitated targeted drug discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

PD-1-Targeted Discovery of Peptide Inhibitors by Virtual Screening, Molecular Dynamics Simulation, and Surface Plasmon Resonance

The blockade of the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) pathway plays a critical role in cancer immunotherapy by reducing the immune escape. Five monoclonal antibodies that antagonized PD-1/PD-L1 interaction have been approved by the Food and Drug Administration (FDA) and marketed as immunotherapy for cancer treatment. However, some weaknesses of antibodies, such as high cost, low stability, poor amenability for oral administration, and immunogenicity, should not be overlooked. To overcome these disadvantages, small-molecule inhibitors targeting PD-L1 were developed. In the present work, we applied in silico and in vitro approaches to develop short peptides targeting PD-1 as chemical probes for the inhibition of PD-1-PD-L1 interaction. We first predicted the potential binding pocket on PD-1/PD-L1 protein-protein interface (PPI). Sequentially, we carried out virtual screening against our in-house peptide library to identify potential ligands. WANG-003, WANG-004, and WANG-005, three of our in-house peptides, were predicted to bind to PD-1 with promising docking scores. Next, we conducted molecular docking and molecular dynamics (MD) simulation for the further analysis of interactions between our peptides and PD-1. Finally, we evaluated the affinity between peptides and PD-1 by surface plasmon resonance (SPR) binding technology. The present study provides a new perspective for the development of PD-1 inhibitors that disrupt PD-1-PD-L1 interactions. These promising peptides have the potential to be utilized as a novel chemical probe for further studies, as well as providing a foundation for further designs of potent small-molecule inhibitors targeting PD-1.

Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis

Serotonin (5-HT) receptors are proteins involved in various neurological and biological processes, such as aggression, anxiety, appetite, cognition, learning, memory, mood, sleep, and thermoregulation. They are commonly associated with drug abuse and addiction due to their importance as targets for various pharmaceutical and recreational drugs. However, due to a high sequence similarity/identity among 5-HT receptors and the unavailability of the 3D structure of the different 5-HT receptor, no report was available so far regarding the systematical comparison of the key and selective residues involved in the binding pocket, making it difficult to design subtype-selective serotonergic drugs. In this work, we first built and validated three-dimensional models for all 5-HT receptors based on the existing crystal structures of 5-HT_1B, 5-HT_2B, and 5-HT_2C. Then, we performed molecular docking studies between 5-HT receptors agonists/inhibitors and our 3D models. The results from docking were consistent with the known binding affinities of each model. Sequentially, we compared the binding pose and selective residues among 5-HT receptors. Our results showed that the affinity variation could be potentially attributed to the selective residues located in the binding pockets. Moreover, we performed MD simulations for 12 5-HT receptors complexed with ligands; the results were consistent with our docking results and the reported data. Finally, we carried out off-target prediction and blood-brain barrier (BBB) prediction for Captagon using our established hallucinogen-related chemogenomics knowledgebase and in-house computational tools, with the hope to provide more information regarding the use of Captagon. We showed that 5-HT_2C, 5-HT_5A, and 5-HT₇ were the most promising targets for Captagon before metabolism. Overall, our findings can provide insights into future drug discovery and design of medications with high specificity to the individual 5-HT receptor to decrease the risk of addiction and prevent drug abuse.

Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose

The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdoses, while millions more suffer from opioid prescription addiction. Recently, drug overdoses caused by fentanyl-laced cocaine specifically are on the rise. Due to drug synergy and an increase in side effects, polydrug addiction can cause more risk than addiction to a single drug. In the present work, we systematically analyzed the overdose and addiction mechanism of cocaine and fentanyl. First, we applied our established chemogenomics knowledgebase and machine-learning-based methods to map out the potential and known proteins, transporters, and metabolic enzymes and the potential therapeutic target(s) for cocaine and fentanyl. Sequentially, we looked into the detail of (1) the addiction to cocaine and fentanyl by binding to the dopamine transporter and the μ opioid receptor (DAT and μOR, respectively), (2) the potential drug-drug interaction of cocaine and fentanyl via p-glycoprotein (P-gp) efflux, (3) the metabolism of cocaine and fentanyl in CYP3A4, and (4) the physiologically based pharmacokinetic (PBPK) model for two drugs and their drug-drug interaction at the absorption, distribution, metabolism, and excretion (ADME) level. Finally, we looked into the detail of JWH133, an agonist of cannabinoid 2-receptor (CB2) with potential as a therapy for cocaine and fentanyl overdose. All these results provide a better understanding of fentanyl and cocaine polydrug addiction and future drug abuse prevention.

A novel small-molecule antagonizes PRMT5-mediated KLF4 methylation for targeted therapy

Background

Triple negative breast cancers (TNBCs) have a poor prognosis and are not amenable to endocrine- or HER2-targeted therapies. The malignant and invasive feature of TNBCs is correlated with its high cancer stem cell population. Recent results from us and others have unveiled an oncogenic role for the PRMT5-KLF4 axis in regulating tumor progression by orchestrating the stemness in mammary tumor cell as well as genome stability. Methylation of KLF4 by PRMT5 leads to KLF4 stabilization, resulting in promoting mitogenesis.

Methods

We have developed a small molecule inhibitor, WX2–43, that specifically intercepts the interaction between PRMT5 and KLF4, thereby enhancing KLF4 degradation.

Findings

Results from our characterization demonstrate that WX2–43 binds to the region between amino acids L400-M500 on PRMT5. Degradation of KLF4 down-regulates KLF4-mediated genes transcription. We have characterized the potent effect for WX2–43 in inhibiting PRMT5-KLF4 binding that, in turns, suppresses tumor progression and induces tumor cell death in both TNBC cultured-cell and animal models.

Interpretation

WX2–43-mediated inhibition of KLF4 methylation by PRMT5 could be a potential strategy for anti-TNBC treatment.

Fund

This work was supported, in whole or in part, by National Institutes of Health grants CA202963 and CA202948 (Wan), R21HL109654 (Xie), P30DA035778 (Xie and Bahar) and P41GM103712 (Bahar).

Insight of Captagon Abuse by Chemogenomics Knowledgebase-guided Systems Pharmacology Target Mapping Analyses

Captagon, known by its genetic name Fenethylline, is an addictive drug that complicates the War on Drugs. Captagon has a strong CNS stimulating effect than its primary metabolite, Amphetamine. However, multi-targets issues associated with the drug and metabolites as well as its underlying mechanisms have not been fully defined. In the present work, we applied our established drug-abuse chemogenomics-knowledgebase systems pharmacology approach to conduct targets/off-targets mapping (SP-Targets) investigation of Captagon and its metabolites for hallucination addiction, and also analyzed the cell signaling pathways for both Amphetamine and Theophylline with data mining of available literature. Of note, Amphetamine, an agonist for trace amine-associated receptor 1 (TAAR1) with enhancing dopamine signaling (increase of irritability, aggression, etc.), is the main cause of Captagon addiction; Theophylline, an antagonist that blocks adenosine receptors (e.g. A2aR) in the brain responsible for restlessness and painlessness, may attenuate the behavioral sensitization caused by Amphetamine. We uncovered that Theophylline's metabolism and elimination could be retarded due to competition and/or blockage of the CYP2D6 enzyme by Amphetamine; We also found that the synergies between these two metabolites cause Captagon's psychoactive effects to act faster and far more potently than those of Amphetamine alone. We carried out further molecular docking modeling and molecular dynamics simulation to explore the molecular interactions between Amphetamine and Theophylline and their important GPCRs targets, including TAAR1 and adenosine receptors. All of the systems pharmacology analyses and results will shed light insight into a better understanding of Captagon addiction and future drug abuse prevention.

An insight into paracetamol and its metabolites using molecular docking and molecular dynamics simulation

Paracetamol is a relatively safe analgesia/antipyretic drug without the risks of addiction, dependence, tolerance, and withdrawal when used alone. However, when administrated in an opioid/paracetamol combination product, which often contains a large quantity of paracetamol, it can be potentially dangerous due to the risk of hepatotoxicity. Paracetamol is known to be metabolized into N-(4-hydroxyphenyl)-arachidonamide (AM404) via fatty acid amide hydrolase (FAAH) and into N-acetyl-p-benzoquinone imine (NAPQI) via cytochrome P450 (CYP) enzymes. However, the underlying mechanism of paracetamol is still unclear. In addition, paracetamol has the potential to interact with other drugs that are also involved with CYP family enzymes (inducer/inhibitor/substrate), an example being illicit drugs. In our present work, we looked into the relationship between paracetamol and its metabolites (AM404 and NAPQI) using molecular docking and molecular dynamics (MD) simulations. We first carried out a series of molecular docking studies between paracetamol/AM404/NAQPI and their reported targets, including CYP 2E1, FAAH, TRPA1, CB1, and TRPV1. Subsequently, we performed MD simulations and energy decomposition for CB1-AM404, TRPV1-AM404, and TRPV1-NAPQI for further investigation of the dynamics interactions. Finally, we summarized and discussed the reported drug-drug interactions between paracetamol and central nervous system drugs, especially illicit drugs. Overall, we are able to provide new insights into the structural and functional roles of paracetamol and its metabolites that can inform the potential prevention and treatment of paracetamol overdose. Graphical abstract Paracetamol and its metabolites.

Allosteric Modulation of Intact γ-Secretase Structural Dynamics

As a protease complex involved in the cleavage of amyloid precursor proteins that lead to the formation of amyloid β fibrils implicated in Alzheimer's disease, γ-secretase is an important target for developing therapeutics against Alzheimer's disease. γ-secretase is composed of four subunits: nicastrin (NCT) in the extracellular (EC) domain, presenilin-1 (PS1), anterior pharynx defective 1, and presenilin enhancer 2 in the transmembrane (TM) domain. NCT and PS1 play important roles in binding amyloid β precursor proteins and modulating PS1 catalytic activity. Yet, the molecular mechanisms of coupling between substrate/modulator binding and catalytic activity remain to be elucidated. Recent determination of intact human γ-secretase cryo-electron microscopy structure has opened the way for a detailed investigation of the structural dynamics of this complex. Our analysis, based on a membrane-coupled anisotropic network model, reveals two types of NCT motions, bending and twisting, with respect to PS1. These underlie the fluctuations between the "open" and "closed" states of the lid-like NCT with respect to a hydrophilic loop 1 (HL1) on PS1, thus allowing or blocking access of the substrate peptide (EC portion) to HL1 and to the neighboring helix TM2. In addition to this alternating access mechanism, fluctuations in the volume of the PS1 central cavity facilitate the exposure of the catalytic site for substrate cleavage. Druggability simulations show that γ-secretase presents several hot spots for either orthosteric or allosteric inhibition of catalytic activity, consistent with experimental data. In particular, a hinge region at the interface between the EC and TM domains, near the interlobe groove of NCT, emerges as an allo-targeting site that would impact the coupling between HL1/TM2 and the catalytic pocket, opening, to our knowledge, new avenues for structure-based design of novel allosteric modulators of γ-secretase protease activity.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Discovery of novel frizzled-7 inhibitors by targeting the receptor's transmembrane domain

Frizzled (Fzd) proteins are seven transmembrane receptors that belong to a novel and separated family of G-protein-coupled receptors (GPCRs). The Fzd receptors can respond to Wnt proteins to activate the canonical β-catenin pathway which is important for both initiation and progression of cancers. Disruption of the Wnt/β-catenin signal thus represents an opportunity for rational cancer prevention and therapy. Of the 10 members of the Fzd family, Fzd7 is the most important member involved in cancer development and progression. In the present studies, we applied structure-based virtual screening targeting the transmembrane domain (TMD) of Fzd7 to select compounds that could potentially bind to the Fzd7-TMD and block the Wnt/Fzd7 signaling and further evaluated them in biological assays. Six small molecule compounds were confirmed as Fzd7 inhibitors. The best hit, SRI37892, significantly blocked the Wnt/Fzd7 signaling with IC₅₀ values in the sub-micromolar range and inhibited cancer cell proliferation with IC₅₀ values around 2 μM. Our results provide the first proof of concept of targeting Fzd-TMD for the development of Wnt/Fzd modulators. The identified small molecular Fzd7 inhibitors can serve as a useful tool for studying the regulation mechanism(s) of Wnt/Fzd7 signaling as well as a starting point for the development of cancer therapeutic agents.

p62/SQSTM1/Sequestosome-1 is an N-recognin of the N-end rule pathway which modulates autophagosome biogenesis

Macroautophagy mediates the selective degradation of proteins and non-proteinaceous cellular constituents. Here, we show that the N-end rule pathway modulates macroautophagy. In this mechanism, the autophagic adapter p62/SQSTM1/Sequestosome-1 is an N-recognin that binds type-1 and type-2 N-terminal degrons (N-degrons), including arginine (Nt-Arg). Both types of N-degrons bind its ZZ domain. By employing three-dimensional modeling, we developed synthetic ligands to p62 ZZ domain. The binding of Nt-Arg and synthetic ligands to ZZ domain facilitates disulfide bond-linked aggregation of p62 and p62 interaction with LC3, leading to the delivery of p62 and its cargoes to the autophagosome. Upon binding to its ligand, p62 acts as a modulator of macroautophagy, inducing autophagosome biogenesis. Through these dual functions, cells can activate p62 and induce selective autophagy upon the accumulation of autophagic cargoes. We also propose that p62 mediates the crosstalk between the ubiquitin-proteasome system and autophagy through its binding Nt-Arg and other N-degrons.Soluble misfolded proteins that fail to be degraded by the ubiquitin proteasome system (UPS) are redirected to autophagy via specific adaptors, such as p62. Here the authors show that p62 recognises N-degrons in these proteins, acting as a N-recognin from the proteolytic N-end rule pathway, and targets these cargos to autophagosomal degradation.

Discovery of Novel Ligands for TNF-α and TNF Receptor-1 through Structure-Based Virtual Screening and Biological Assay

Tumor necrosis factor α (TNF-α) is overexpressed in various diseases, and it has been a validated therapeutic target for autoimmune diseases. All therapeutics currently used to target TNF-α are biomacromolecules, and limited numbers of TNF-α chemical inhibitors have been reported, which makes the identification of small-molecule alternatives an urgent need. Recent studies have mainly focused on identifying small molecules that directly bind to TNF-α or TNF receptor-1 (TNFR1), inhibit the interaction between TNF-α and TNFR1, and/or regulate related signaling pathways. In this study, we combined in silico methods with biophysical and cell-based assays to identify novel antagonists that bind to TNF-α or TNFR1. Pharmacophore model filtering and molecular docking were applied to identify potential TNF-α antagonists. In regard to TNFR1, we constructed a three-dimensional model of the TNF-α-TNFR1 complex and carried out molecular dynamics simulations to sample the conformations. The residues in TNF-α that have been reported to play important roles in the TNF-α-TNFR1 complex were removed to form a pocket for further virtual screening of TNFR1-binding ligands. We obtained 20 virtual hits and tested them using surface plasmon resonance-based assays, which resulted in one ligand that binds to TNFR1 and four ligands with different scaffolds that bind to TNF-α. T1 and R1, the two most active compounds with Kd values of 11 and 16 μM for TNF-α and TNFR1, respectively, showed activities similar to those of known antagonists. Further cell-based assays also demonstrated that T1 and R1 have similar activities compared to the known TNF-α antagonist C87. Our work has not only produced several TNF-α and TNFR1 antagonists with novel scaffolds for further structural optimization but also showcases the power of our in silico methods for TNF-α- and TNFR1-based drug discovery.

Structural Analysis of Chemokine Receptor-Ligand Interactions

This review focuses on the construction and application of structural chemokine receptor models for the elucidation of molecular determinants of chemokine receptor modulation and the structure-based discovery and design of chemokine receptor ligands. A comparative analysis of ligand binding pockets in chemokine receptors is presented, including a detailed description of the CXCR4, CCR2, CCR5, CCR9, and US28 X-ray structures, and their implication for modeling molecular interactions of chemokine receptors with small-molecule ligands, peptide ligands, and large antibodies and chemokines. These studies demonstrate how the integration of new structural information on chemokine receptors with extensive structure–activity relationship and site-directed mutagenesis data facilitates the prediction of the structure of chemokine receptor–ligand complexes that have not been crystallized. Finally, a review of structure-based ligand discovery and design studies based on chemokine receptor crystal structures and homology models illustrates the possibilities and challenges to find novel ligands for chemokine receptors.

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

Synthesis, molecular modeling and SAR study of novel pyrazolo[5,1-f][1,6]naphthyridines as CB2 receptor antagonists/inverse agonists

Pyrazolo[5,1-f][1,6]naphthyridine-carboxamide derivatives were synthesized and evaluated for the affinity at CB₁ and CB₂ receptors. Based on the AgOTf and proline-cocatalyzed multicomponent methodology, the ethyl 5-(p-tolyl)pyrazolo[5,1-f][1,6]naphthyridine-2-carboxylate (12) and ethyl 5-(2,4-dichlorophenyl)pyrazolo[5,1-f][1,6]naphthyridine-2-carboxylate (13) intermediates were synthesized from the appropriate o-alkynylaldehydes, p-toluenesulfonyl hydrazide and ethyl pyruvate. Most of the novel compounds feature a p-tolyl (8a-i) or a 2,4-dichlorophenyl (8j) motif at the C₅-position of the tricyclic pyrazolo[5,1-f][1,6]naphthyridine scaffold. Structural variation on the carboxamide moiety at the C₂-position includes basic monocyclic, terpenoid and adamantine-based amines. Among these derivatives, compound 8h (N-adamant-1-yl-5-(p-tolyl)pyrazolo[5,1-f][1,6]naphthyridine-2-carboxamide) exhibited the highest CB₂ receptor affinity (K_i=33nM) and a high degree of selectivity (K_iCB₁/K_iCB₂=173:1), whereas a similar trend in the near nM range was seen for the bornyl analogue (compound 8f, K_i=53nM) and the myrtanyl derivative 8j (K_i=67nM). Effects of 8h, 8f and 8j on forskolin-stimulated cAMP levels were determined, showing antagonist/inverse agonist properties for such compounds. Docking studies conducted for these derivatives and the reference antagonist/inverse agonist compound 4 (SR144528) disclosed the specific pattern of interactions probably related to the pyrazolo[5,1-f][1,6]naphthyridine scaffold as CB₂ inverse agonists.

Multi-Functional Diarylurea Small Molecule Inhibitors of TRPV1 with Therapeutic Potential for Neuroinflammation

Transient receptor potential vanilloid type 1 (TRPV1), a heat-sensitive calcium channel protein, contributes to inflammation as well as to acute and persistent pain. Since TRPV1 occupies a central position in pathways of neuronal inflammatory signaling, it represents a highly attractive potential therapeutic target for neuroinflammation. In the present work, we have in silico identified a series of diarylurea analogues for hTRPV1, of which 11 compounds showed activity in the nanomolar to micromolar range as validated by in vitro biological assays. Then, we utilized molecular docking to explore the detailed interactions between TRPV1 and the compounds to understand the contributions of the different substituent groups. Tyr511, Leu518, Leu547, Thr550, Asn551, Arg557, and Leu670 were important for the recognition of the small molecules by TRPV1. A hydrophobic group in R2 or a polar/hydrophilic group in R1 contributed significantly to the activities of the antagonists at TRPV1. In addition, the subtle different binding pose of meta-chloro in place of para-fluoro in the R2 group converted antagonism into partial agonism, as was predicted by our short-term molecular dynamics (MD) simulation and validated by bioassay. Importantly, compound 15, one of our best TRPV1 inhibitors, also showed potential binding affinity (1.39 μM) at cannabinoid receptor 2 (CB2), which is another attractive target for immuno-inflammation diseases. Furthermore, compound 1 and its diarylurea analogues were predicted to target the C-X-C chemokine receptor 2 (CXCR2), although bioassay validation of CXCR2 with these compounds still needs to be performed. This prediction from the modeling is of interest, since CXCR2 is also a potential therapeutic target for chronic inflammatory diseases. Our findings provide novel strategies to develop a small molecule inhibitor to simultaneously target two or more inflammation-related proteins for the treatment of a wide range of inflammatory disorders including neuroinflammation and neurodegenerative diseases with potential synergistic effect.

Difference and Influence of Inactive and Active States of Cannabinoid Receptor Subtype CB2: From Conformation to Drug Discovery

Cannabinoid receptor 2 (CB2), a G protein-coupled receptor (GPCR), is a promising target for the treatment of neuropathic pain, osteoporosis, immune system, cancer, and drug abuse. The lack of an experimental three-dimensional CB2 structure has hindered not only the development of studies of conformational differences between the inactive and active CB2 but also the rational discovery of novel functional compounds targeting CB2. In this work, we constructed models of both inactive and active CB2 by homology modeling. Then we conducted two comparative 100 ns molecular dynamics (MD) simulations on the two systems-the active CB2 bound with both the agonist and G protein and the inactive CB2 bound with inverse agonist-to analyze the conformational difference of CB2 proteins and the key residues involved in molecular recognition. Our results showed that the inactive CB2 and the inverse agonist remained stable during the MD simulation. However, during the MD simulations, we observed dynamical details about the breakdown of the "ionic lock" between R131(3.50) and D240(6.30) as well as the outward/inward movements of transmembrane domains of the active CB2 that bind with G proteins and agonist (TM5, TM6, and TM7). All of these results are congruent with the experimental data and recent reports. Moreover, our results indicate that W258(6.48) in TM6 and residues in TM4 (V164(4.56)-L169(4.61)) contribute greatly to the binding of the agonist on the basis of the binding energy decomposition, while residues S180-F183 in extracellular loop 2 (ECL2) may be of importance in recognition of the inverse agonist. Furthermore, pharmacophore modeling and virtual screening were carried out for the inactive and active CB2 models in parallel. Among all 10 hits, two compounds exhibited novel scaffolds and can be used as novel chemical probes for future studies of CB2. Importantly, our studies show that the hits obtained from the inactive CB2 model mainly act as inverse agonist(s) or neutral antagonist(s) at low concentration. Moreover, the hit from the active CB2 model also behaves as a neutral antagonist at low concentration. Our studies provide new insight leading to a better understanding of the structural and conformational differences between two states of CB2 and illuminate the effects of structure on virtual screening and drug design.

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

Discovery of novel INK4C small-molecule inhibitors to promote human and murine hematopoietic stem cell ex vivo expansion

Hematopoietic stem cells (HSCs) have emerged as promising therapeutic cell sources for high-risk hematological malignancies and immune disorders. However, their clinical use is limited by the inability to expand these cells ex vivo. Therefore, there is an urgent need to identify specific targets and effective probes that can expand HSCs. Here we report a novel class of INK4C (p18^INK4C or p18) small molecule inhibitors (p18SMIs), which were initially found by in silico 3D screening. We identified a lead p18 inhibitor, XIE18-6, confirmed its p18-targeting specificity and bioactivity of promoting HSCs expansion, and then performed structure-activity relationship (SAR) studies by synthesizing a series of analogs of XIE18–6. Among these, compound 40 showed the most potent bioactivity in HSCs expansion (ED₅₀ = 5.21 nM). We confirmed that compound 40 promoted expansion of both murine and human HSCs, and also confirmed its p18-targeting specificity. Notably, compound 40 did not show significant cytotoxicity toward 32D cells or HSCs, nor did it augment leukemia cell proliferation. Taken together, our newly discovered p18SMIs represent novel chemical agents for murine and human HSCs ex vivo expansion and also can be used as valuable chemical probes for further HSC biology research towards promising utility for therapeutic purposes.

HU-446 and HU-465, Derivatives of the Non-psychoactive Cannabinoid Cannabidiol, Decrease the Activation of Encephalitogenic T Cells

Cannabidiol (CBD), the non-psychoactive cannabinoid, has been previously shown by us to decrease peripheral inflammation and neuroinflammation in mouse experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS). Here we have studied the anti-inflammatory effects of newly synthesized derivatives of natural (-)-CBD ((-)-8,9-dihydro-7-hydroxy-CBD; HU-446) and of synthetic (+)-CBD ((+)-8,9-dihydro-7-hydroxy-CBD; HU-465) on activated myelin oligodendrocyte glycoprotein (MOG)35-55-specific mouse encephalitogenic T cells (T(MOG) ) driving EAE/MS-like pathologies. Binding assays followed by molecular modeling revealed that HU-446 has negligible affinity toward the cannabinoid CB1 and CB2 receptors while HU-465 binds to both CB1 and CB2 receptors at the high nanomolar concentrations (Ki = 76.7 ± 5.8 nm and 12.1 ± 2.3 nm, respectively). Both, HU-446 and HU-465, at 5 and 10 μm (but not at 0.1 and 1 μm), inhibited the MOG35-55-induced proliferation of autoreactive T(MOG) cells via CB1/CB2 receptor independent mechanisms. Moreover, both HU-446 and HU-465, at 5 and 10 μm, inhibited the release of IL-17, a key autoimmune cytokine, from MOG35-55-stimulated T(MOG) cells. These results suggest that HU-446 and HU-465 have anti-inflammatory potential in inflammatory and autoimmune diseases.

Allosteric Binding Site and Activation Mechanism of Class C G-Protein Coupled Receptors: Metabotropic Glutamate Receptor Family

Metabotropic glutamate receptors (mGluR) are mainly expressed in the central nervous system (CNS) and contain eight receptor subtypes, named mGluR1 to mGluR8. The crystal structures of mGluR1 and mGluR5 that are bound with the negative allosteric modulator (NAM) were reported recently. These structures provide a basic model for all class C of G-protein coupled receptors (GPCRs) and may aid in the design of new allosteric modulators for the treatment of CNS disorders. However, these structures are only combined with NAMs in the previous reports. The conformations that are bound with positive allosteric modulator (PAM) or agonist of mGluR1/5 remain unknown. Moreover, the structural information of the other six mGluRs and the comparisons of the mGluRs family have not been explored in terms of their binding pockets, the binding modes of different compounds, and important binding residues. With these crystal structures as the starting point, we built 3D structural models for six mGluRs by using homology modeling and molecular dynamics (MD) simulations. We systematically compared their allosteric binding sites/pockets, the important residues, and the selective residues by using a series of comparable dockings with both the NAM and the PAM. Our results show that several residues played important roles for the receptors' selectivity. The observations of detailed interactions between compounds and their correspondent receptors are congruent with the specificity and potency of derivatives or compounds bioassayed in vitro. We then carried out 100 ns MD simulations of mGluR5 (residue 26-832, formed by Venus Flytrap domain, a so-called cysteine-rich domain, and 7 trans-membrane domains) bound with antagonist/NAM and with agonist/PAM. Our results show that both the NAM and the PAM seemed stable in class C GPCRs during the MD. However, the movements of "ionic lock," of trans-membrane domains, and of some activation-related residues in 7 trans-membrane domains of mGluR5 were congruent with the findings in class A GPCRs. Finally, we selected nine representative bound structures to perform 30 ns MD simulations for validating the stabilities of interactions, respectively. All these bound structures kept stable during the MD simulations, indicating that the binding poses in this present work are reasonable. We provided new insight into better understanding of the structural and functional roles of the mGluRs family and facilitated the future structure-based design of novel ligands of mGluRs family with therapeutic potential.

Design and activity of AP endonuclease-1 inhibitors

Apurinic/apyrimidinic endonuclease-1/redox effector factor-1 (APE-1) is a critical component of base excision repair that excises abasic lesions created enzymatically by the action of DNA glycosylases on modified bases and non-enzymatically by hydrolytic depurination/depyrimidination of nucleobases. Many anticancer drugs generate DNA adducts that are processed by base excision repair, and tumor resistance is frequently associated with enhanced APE-1 expression. Accordingly, APE-1 is a potential therapeutic target to treat cancer. Using computational approaches and the high resolution structure of APE-1, we developed a 5-point pharmacophore model for APE-1 small molecule inhibitors. One of the nM APE-1 inhibitors (AJAY-4) that was identified based on this model exhibited an overall median growth inhibition (GI50) of 4.19 μM in the NCI-60 cell line panel. The mechanism of action is shown to be related to the buildup of abasic sites that cause PARP activation and PARP cleavage, and the activation of caspase-3 and caspase-7, which is consistent with cell death by apoptosis. In a drug combination growth inhibition screen conducted in 10 randomly selected NCI-60 cell lines and with 20 clinically used non-genotoxic anticancer drugs, a synergy was flagged in the SK-MEL-5 melanoma cell line exposed to combinations of vemurafenib, which targets melanoma cells with V600E mutated BRAF, and AJAY-4, our most potent APE-1 inhibitor. The synergy between AJAY-4 and vemurafenib was not observed in cell lines expressing wild-type B-Raf protein. This synergistic combination may provide a solution to the resistance that develops in tumors treated with B-Raf-targeting drugs.

Structural insight into tetrameric hTRPV1 from homology modeling, molecular docking, molecular dynamics simulation, virtual screening, and bioassay validations

The transient receptor potential vanilloid type 1 (TRPV1) is a heat-activated cation channel protein, which contributes to inflammation, acute and persistent pain. Antagonists of human TRPV1 (hTRPV1) represent a novel therapeutic approach for the treatment of pain. Developing various antagonists of hTRPV1, however, has been hindered by the unavailability of a 3D structure of hTRPV1. Recently, the 3D structures of rat TRPV1 (rTRPV1) in the presence and absence of ligand have been reported as determined by cryo-EM. rTRPV1 shares 85.7% sequence identity with hTRPV1. In the present work, we constructed and reported the 3D homology tetramer model of hTRPV1 based on the cryo-EM structures of rTRPV1. Molecular dynamics (MD) simulations, energy minimizations, and prescreen were applied to select and validate the best model of hTRPV1. The predicted binding pocket of hTRPV1 consists of two adjacent monomers subunits, which were congruent with the experimental rTRPV1 data and the cyro-EM structures of rTRPV1. The detailed interactions between hTRPV1 and its antagonists or agonists were characterized by molecular docking, which helped us to identify the important residues. Conformational changes of hTRPV1 upon antagonist/agonist binding were also explored by MD simulation. The different movements of compounds led to the different conformational changes of monomers in hTRPV1, indicating that TRPV1 works in a concerted way, resembling some other channel proteins such as aquaporins. We observed that the selective filter was open when hTRPV1 bound with an agonist during MD simulation. For the lower gate of hTRPV1, we observed large similarities between hTRPV1 bound with antagonist and with agonist. A five-point pharmacophore model based on several antagonists was established, and the structural model was used to screen in silico for new antagonists for hTRPV1. By using the 3D TRPV1 structural model above, the pilot in silico screening has begun to yield promising hits with activity as hTRPV1 antagonists, several of which showed substantial potency.

Small-molecule inhibitors targeting INK4 protein p18(INK4C) enhance ex vivo expansion of haematopoietic stem cells

Among cyclin-dependent kinase inhibitors that control the G1 phase in cell cycle, only p18 and p27 can negatively regulate haematopoietic stem cell (HSC) self-renewal. In this manuscript, we demonstrate that p18 protein is a more potent inhibitor of HSC self-renewal than p27 in mouse models and its deficiency promoted HSC expansion in long-term culture. Single-cell analysis indicated that deleting p18 gene favoured self-renewing division of HSC in vitro. Based on the structure of p18 protein and in-silico screening, we further identified novel smallmolecule inhibitors that can specifically block the activity of p18 protein. Our selected lead compounds were able to expand functional HSCs in a short-term culture. Thus, these putative small-molecule inhibitors for p18 protein are valuable for further dissecting the signalling pathways of stem cell self-renewal and may help develop more effective chemical agents for therapeutic expansion of HSC.

Targeting cannabinoid receptor-2 pathway by phenylacetylamide suppresses the proliferation of human myeloma cells through mitotic dysregulation and cytoskeleton disruption

Cannabinoid receptor-2 (CB2) is expressed dominantly in the immune system, especially on plasma cells. Cannabinergic ligands with CB2 selectivity emerge as a class of promising agents to treat CB2-expressing malignancies without psychotropic concerns. In this study, we found that CB2 but not CB1 was highly expressed in human multiple myeloma (MM) and primary CD138+ cells. A novel inverse agonist of CB2, phenylacetylamide but not CB1 inverse agonist SR141716, inhibited the proliferation of human MM cells (IC50 : 0.62 ∼ 2.5 μM) mediated by apoptosis induction, but exhibited minor cytotoxic effects on human normal mononuclear cells. CB2 gene silencing or pharmacological antagonism markedly attenuated phenylacetylamide's anti-MM effects. Phenylacetylamide triggered the expression of C/EBP homologous protein at the early treatment stage, followed by death receptor-5 upregulation, caspase activation, and β-actin/tubulin degradation. Cell cycle related protein cdc25C and mitotic regulator Aurora A kinase were inactivated by phenylacetylamide treatment, leading to an increase in the ratio inactive/active cdc2 kinase. As a result, phosphorylation of CDK substrates was decreased, and the MM cell mitotic division was largely blocked by treatment. Importantly, phenylacetylamide could overcome the chemoresistance of MM cells against dexamethasone or melphalan. Thus, targeting CB2 may represent an attractive approach to treat cancers of immune origin.

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR)

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

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, Val113(3.32), and predicted two new residues, Phe183 in ECL2 and Phe281(7.35), 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.

Quinoline derivatives: candidate drugs for a class B G-protein coupled receptor, the calcitonin gene-related peptide receptor, a cause of migraines

Class B G-protein coupled receptors are involved in a wide variety of diseases and are a major focus in drug design. Migraines are a common problem, and one of their major causative agents is the class B G-protein coupled receptor, Calcitonin gene-related peptide (CGRP) receptor, a target for competitive drug discovery. The calcitonin receptor-like receptor generates complexes with a receptor activity-modifying protein, which determines the type of receptor protein formed. The CGRP receptor comprises a complex formed from the calcitonin receptor-like receptor and receptor activity-modifying protein 1. In this study, an in silico docking approach was used to target the calcitonin receptor-like receptor in the bound form with receptor activity-modifying protein 1 (CGRP receptor), as well as in the unbound form. In both cases, the resulting inhibitors bound to the same cavity of the calcitonin receptor-like receptor. The twelve evaluated compounds were competitive inhibitors and showed efficient inhibitory activity against the CGRP receptor and Calcitonin receptor-like receptor. The two studied quinoline derivatives demonstrated potentially ideal inhibitory activity in terms of binding interactions and low range nano-molar inhibition constants. These compounds could prove helpful in designing drugs for the effective treatment of migraines. We propose that quinoline derivatives possess inhibitory activity by disturbing CGRP binding in the trigeminovascular system and may be considered for further preclinical appraisal for the treatment of migraines.

Examining the critical roles of human CB2 receptor residues Valine 3.32 (113) and Leucine 5.41 (192) in ligand recognition and downstream signaling activities

We performed molecular modeling and docking to predict a putative binding pocket and associated ligand-receptor interactions for human cannabinoid receptor 2 (CB2). Our data showed that two hydrophobic residues came in close contact with three structurally distinct CB2 ligands: CP-55,940, SR144528 and XIE95-26. Site-directed mutagenesis experiments and subsequent functional assays implicated the roles of Valine residue at position 3.32 (V113) and Leucine residue at position 5.41 (L192) in the ligand binding function and downstream signaling activities of the CB2 receptor. Four different point mutations were introduced to the wild type CB2 receptor: V113E, V113L, L192S and L192A. Our results showed that mutation of Val113 with a Glutamic acid and Leu192 with a Serine led to the complete loss of CB2 ligand binding as well as downstream signaling activities. Substitution of these residues with those that have similar hydrophobic side chains such as Leucine (V113L) and Alanine (L192A), however, allowed CB2 to retain both its ligand binding and signaling functions. Our modeling results validated by competition binding and site-directed mutagenesis experiments suggest that residues V113 and L192 play important roles in ligand binding and downstream signaling transduction of the CB2 receptor.

64Cu-labeled somatostatin analogues conjugated with cross-bridged phosphonate-based chelators via strain-promoted click chemistry for PET imaging: in silico through in vivo studies

Somatostatin receptor subtype 2 (sstr2) is a G-protein-coupled receptor (GPCR) that is overexpressed in neuroendocrine tumors. The homology model of sstr2 was built and was used to aid the design of new somatostatin analogues modified with phosphonate-containing cross-bridged chelators for evaluation of using them as PET imaging radiopharmaceuticals. The new generation chelators were conjugated to Tyr³-octreotate (Y3-TATE) through bioorthogonal, strain-promoted alkyne azide cycloaddition (SPAAC) to form CB-TE1A1P–DBCO–Y3-TATE (AP) and CB-TE1K1P–PEG4–DBCO–Y3-TATE (KP) in improved yields compared to standard direct conjugation methods of amide bond formation. Consistent with docking studies, the clicked bioconjugates showed high binding affinities to sstr2, with K_d values ranging from 0.6 to 2.3 nM. Selected isomers of the clicked products were used in biodistribution and PET/CT imaging. Introduction of the bulky dibenzocyclooctyne group in AP decreased clearance rates from circulation. However, the additional carboxylate group and PEG linker from the KP conjugate significantly improved labeling conditions and in vivo stability of the copper complex and ameliorated the slower pharmacokinetics of the clicked somatostatin analogues.

Network pharmacology analyses of the antithrombotic pharmacological mechanism of Fufang Xueshuantong Capsule with experimental support using disseminated intravascular coagulation rats

ETHNOPHARMACOLOGICAL RELEVANCE

Fufang Xueshuantong (FXST) Capsule is developed on a traditional Chinese medicine remedy, with a four-herb formula of Panax notoginseng, Radix astragali, Salvia miltiorrhizae and Radix scrophulariaceae. It has been used for treatment of the clinic cardiovascular disease for many years.

MATERIALS AND METHODS

Due to its complexity of compositions and polypharmacological effects, it often complicates understanding of the mechanisms of action. In the present work, we have constructed an integrated model of system pharmacology to investigate the polypharmacological mechanisms of FXST formulation for treatment of thrombosis disease.

RESULTS

The predicted results showed that 22 ingredients in FXST were closely associated with 41 protein targets related to blood coagulation, fibrinolysis and platelet aggregation. Through analysis of the compound-protein target association, significant cross-targets between each herb indicated the multiple active chemical ingredients might interact with the same target simultaneously and thus explained the synergistic mechanisms of the principle of Traditional Chinese medicines (TCMs) as ''Jun (emperor) - Chen (minister) - Zuo (adjuvant) - Shi (courier)''. To validate the polypharmacological effects predicted by our network pharmacology (NetPharm) analysis, we have carried out experimental investigation the effects of FXST on the disorders of the blood coagulation system in a lipopolysaccharide-induced disseminated intravascular coagulation (DIC) rat model. The results showed that FXST could significantly ameliorate the activation of coagulation system, which is congruent with the cross-target prediction by NetPharm approach.

CONCLUSIONS

The combined investigations provide more insight into better understanding of the pharmacological mechanisms of FXST, and may also offer an alternative avenue to further explore the chemical and pharmacological basis of TCMs.

Comparative normal/failing rat myocardium cell membrane chromatographic analysis system for screening specific components that counteract doxorubicin-induced heart failure from Acontium carmichaeli

Cell membrane chromatography (CMC) derived from pathological tissues is ideal for screening specific components acting on specific diseases from complex medicines owing to the maximum simulation of in vivo drug-receptor interactions. However, there are no pathological tissue-derived CMC models that have ever been developed, as well as no visualized affinity comparison of potential active components between normal and pathological CMC columns. In this study, a novel comparative normal/failing rat myocardium CMC analysis system based on online column selection and comprehensive two-dimensional (2D) chromatography/monolithic column/time-of-flight mass spectrometry was developed for parallel comparison of the chromatographic behaviors on both normal and pathological CMC columns, as well as rapid screening of the specific therapeutic agents that counteract doxorubicin (DOX)-induced heart failure from Acontium carmichaeli (Fuzi). In total, 16 potential active alkaloid components with similar structures in Fuzi were retained on both normal and failing myocardium CMC models. Most of them had obvious decreases of affinities on failing myocardium CMC compared with normal CMC model except for four components, talatizamine (TALA), 14-acetyl-TALA, hetisine, and 14-benzoylneoline. One compound TALA with the highest affinity was isolated for further in vitro pharmacodynamic validation and target identification to validate the screen results. Voltage-dependent K⁺ channel was confirmed as a binding target of TALA and 14-acetyl-TALA with high affinities. The online high throughput comparative CMC analysis method is suitable for screening specific active components from herbal medicines by increasing the specificity of screened results and can also be applied to other biological chromatography models.

Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands

Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

Molecular dynamics simulations of the adenosine A2a receptor: structural stability, sampling, and convergence

Molecular dynamics (MD) simulations of membrane-embedded G-protein coupled receptors (GPCRs) have rapidly gained popularity among the molecular simulation community in recent years, a trend which has an obvious link to the tremendous pharmaceutical importance of this group of receptors and the increasing availability of crystal structures. In view of the widespread use of this technique, it is of fundamental importance to ensure the reliability and robustness of the methodologies so they yield valid results and enable sufficiently accurate predictions to be made. In this work, 200 ns simulations of the A2a adenosine receptor (A2a AR) have been produced and evaluated in the light of these requirements. The conformational dynamics of the target protein, as obtained from replicate simulations in both the presence and absence of an inverse agonist ligand (ZM241385), have been investigated and compared using principal component analysis (PCA). Results show that, on this time scale, convergence of the replicates is not readily evident and dependent on the types of the protein motions considered. Thus rates of inter- as opposed to intrahelical relaxation and sampling can be different. When studied individually, we find that helices III and IV have noticeably greater stability than helices I, II, V, VI, and VII in the apo form. The addition of the inverse agonist ligand greatly improves the stability of all helices.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Target based virtual screening by docking into automatically generated GPCR models

Target based virtual screening (VS) combined with high-throughput measurements is an extremely useful tool to identify small molecule hits for proteins and in particular for G-protein coupled receptors (GPCRs). However, this is a quite difficult process for GPCRs due to the paucity of 3D structural information on these receptors. Therefore, the only possibility for target based VS is to build a structural model of the GPCR to be used for docking. However, GPCR model building is a very time consuming process, if the model should be able to explain all experimental findings and this investment is not always justified, if the model is only used for VS. Thus, a fully automated workflow is presented here, where a large number of GPCR models is built, and the best model is identified to be used for docking. The workflow leads to moderate enrichments with a very low effort. The inputs required are the sequence of the targeted GPCR, a reference ligand with experimental information and a database of small molecules to be used for docking. Manual intervention is recommended at various points, but it is strictly speaking not necessary.

LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors

The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.