In silico binding characteristics between human histamine H1 receptor and antagonists

It is widely acknowledged that the H₁ receptor antagonists have important therapeutic significance in the treatment of various allergic disorders, but little was known about the binding mode between the receptor and antagonists since the crystal structure of G-protein coupling receptors (GPCRs) were hard to obtain. In this paper, a theoretical three-dimensional model of human histamine H₁ receptor (HHR1) was developed on the basis of recently reported high resolution structures of human A_2A adenosine receptor, human β₂-adrenoceptor and turkey β₁-adrenoceptor. Furthermore, three representative H₁ receptor antagonists were chosen for docking studies. Subsequently, a qualitative pharmacophore model was developed by Hiphop algorithm based on the docking conformations of these three antagonists. In this paper, active environment, certain key residues, and the corresponding pharmacophore features of H₁ receptor were identified by such combinations of receptor-based and ligand-based approaches, which would give sufficient guidance for the rational design of novel antihistamine agents.     

Molecular modelling study on human histamine H1 receptor and its applications in virtual lead identification for designing novel inverse agonists

Human histamine H1 receptor (HHR1) is one of the G protein-coupled receptors (GPCRs) known for their constitutive activation in the absence of agonist binding. Inverse agonists are the compounds that inhibit this constitutive activity of GPCRs. HHR1 is involved in allergic reactions and is also known to be constitutively active. An updated quantitative pharmacophore model, Hypo1, has been developed using a diverse set of known HHR1 inverse agonists employing the HypoGen algorithm as implemented in Accelrys Discovery Studio 2.1. Hypo1 comprised four pharmacophore features (each one of hydrogen bond acceptor, hydrophobic, ring aromatic and positive ionisable group) along with a high correlation value of 0.944. This pharmacophore model was validated using an external test set containing 25 diverse inverse agonists and CatScramble method. Three chemical databases were screened for novel chemical scaffolds using Hypo1 as a query, to be utilised in drug design. The 3D structure of HHR1 has been constructed using human β2 adrenergic receptor. Molecular docking studies were performed with the database hit compounds using GOLD 4.1 program. The combination of all results led us to identify novel compounds to be deployed in designing new generation HHR1 inverse agonists.     

Pharmacophore identification,synthesis, and biological evaluation of carboxylated chalcone derivatives as CysLT1 antagonists

The pharmacophore model (Hypo1) with a well prediction capacity for CysLT₁ antagonists was developed using Catalyst/HypoGen program. Virtual screening against an in-house database consisted of carboxylated chalcones using Hypo1 was performed. Retrieved hits 26a, 26b, 27a, and 27b were synthesized and biological evaluated, the results of which demonstrated that these compounds showed moderate to good CysLT₁ antagonistic activities. This study indicated that the generated model (Hypo1) is a reliable and useful tool in lead optimization for novel CysLT₁ antagonists.     

In silico investigation of interactions between human cannabinoid receptor-1 and its antagonists

Cannabinoid receptor-1 (CB(1)) is widely expressed in the central nervous system and plays a vital role in regulating food intake and energy expenditure. CB(1) antagonists such as Rimonabant have been used in clinic to inhibit food intake, and therefore reduce body weight in obese animals and humans. To investigate the binding modes of CB(1) antagonists to the receptor, both receptor- and ligand-based methods were implemented in this study. At first, a pharmacophore model was generated based on 31 diverse CB(1) antagonists collected from literature. A test set validation and a simulated virtual screening evaluation were then performed to verify the reliability and discriminating ability of the pharmacophore. Meanwhile, the homology model of CB(1) receptor was constructed based on the crystal structure of human β (2) adrenergic receptor (β (2)-AR). Several classical antagonists were then docked into the optimized homology model with induced fit docking method. A hydrogen bond between the antagonists and Lys192 on the third transmembrane helix of the receptor was formed in the docking study, which has proven to be critical for receptor-ligand interaction by biological experiments. The structure obtained from induced fit docking was then confirmed to be a reliable model for molecular docking from the result of the simulated virtual screening. The consistency between the pharmacophore and the homology structure further proved the previous observation. The built receptor structure and antagonists' pharmacophore should be useful for the understanding of inhibitory mechanism and development of novel CB(1) antagonists.     

Pharmacophore identification of alpha(1A)-adrenoceptor antagonists

A chemical feature based pharmacophore model was developed for alpha(1A)-adrenoceptor antagonists by HypoGen module implemented in catalyst software package. The best scoring pharmacophore hypothesis, Hypo1, consisted of four important chemical features (one positive ion, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group). The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.     

Pharmacophore mapping and in silico screening to identify new potent leads for A2A adenosine receptor as antagonists

A_2A adenosine receptor (AR) antagonists play an important role in neurodegenerative diseases like Parkinson’s disease. A 3D-QSAR study of A_2A AR antagonists, was taken up to design best pharmacophore model. The pharmacophoric features (ADHRR) containing a hydrogen bond acceptor (A), a hydrogen bond donor (D), a hydrophobic group (H) and two aromatic rings (R), is projected as the best predictive pharmacophore model. The QSAR model was further treated as a template for in silico search of databases to identify new scaffolds. The binding patterns of the leads with A_2A AR are analysed using docking studies and novel potent ligands of A_2A AR are projected.     

Chemical function-based pharmacophore generation of selective κ-opioid receptor agonists by catalyst and phase

Two chemical function-based pharmacophore models of selective κ-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent κ-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel κ-agonists.     

Discovery and functional evaluation of diverse novel human CB1 receptor ligands

Ligand-based virtual screening with a 3D pharmacophore led to the discovery of 30 novel, diverse and drug-like ligands of the human cannabinoid receptor 1 (hCB₁). The pharmacophore was validated with a hit rate of 16%, binding selectivity versus hCB₂, and expected functional profiles. The discovered compounds provide new tools for exploring cannabinoid pharmacology.     

A specific pharmacophore model of sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors

Sodium-dependent glucose co-transporter 2 (SGLT2) plays a pivotal role in maintaining glucose equilibrium in the human body, emerging as one of the most promising targets for the treatment of diabetes mellitus type 2. Pharmacophore models of SGLT2 inhibitors have been generated with a training set of 25 SGLT2 inhibitors using Discovery Studio V2.1. The best hypothesis (Hypo1(SGLT2)) contains one hydrogen bond donor, five excluded volumes, one ring aromatic and three hydrophobic features, and has a correlation coefficient of 0.955, cost difference of 68.76, RMSD of 0.85. This model was validated by test set, Fischer randomization test and decoy set methods. The specificity of Hypo1(SGLT2) was evaluated. The pharmacophore features of Hypo1(SGLT2) were different from the best pharmacophore model (Hypo1(SGLT1)) of SGLT1 inhibitors we developed. Moreover, Hypo1(SGLT2) could effectively distinguish selective inhibitors of SGLT2 from those of SGLT1. These results indicate that a highly predictive and specific pharmacophore model of SGLT2 inhibitors has been successfully obtained. Then Hypo1(SGLT2) was used as a 3D query to screen databases including NCI and Maybridge for identifying new inhibitors of SGLT2. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. And several compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents

Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening     

Pharmacophore modeling and virtual screening for designing potential 5-Lipoxygenase inhibitors

Inhibitors of the 5-Lipoxygenase (5-LOX) pathway have a therapeutic potential in a variety of inflammatory disorders such as asthma. In this study, chemical feature based pharmacophore models of inhibitors of 5-LOX have been developed with the aid of HipHop and HypoGen modules within Catalyst program package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.97), consists of two hydrogen-bond acceptors, one hydrophobic feature and one ring aromatic feature. Hypo1 was further validated by test set and cross validation method. The application of the model shows great success in predicting the activities of 65 known 5-LOX inhibitors in our test set with a correlation coefficient of 0.85 with a cross validation of 95% confidence level, proving that the model is reliable in identifying structurally diverse compounds for inhibitory activity against 5-LOX. Furthermore, Hypo1 was used as a 3D query for screening Maybridge and NCI databases within catalyst and also drug like compounds obtained from Enamine Ltd, which follow Lipinski’s rule of five. The hit compounds were subsequently subjected to filtering by docking and visualization, to identify the potential lead molecules. Finally 5 potential lead compounds, identified in the above process, were evaluated for their inhibitory activities. These studies resulted in the identification of two compounds with potent inhibition of 5-LOX activity with IC₅₀ of 14 μM and 35 μM, respectively. These studies thus validate the pharmacophore model generated and suggest the usefulness of the model in screening of various small molecule libraries and identification of potential lead compounds for 5-LOX inhibition.     

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149) and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM) and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.     

Exploring new scaffolds for angiotensin II receptor antagonism

Nowadays, AT₁ receptor (AT₁R) antagonists (ARBs) constitute the one of the most prevalent classes of antihypertensive drugs that modulate the renin-angiotensin system (RAS). Their main uses include also treatment of diabetic nephropathy (kidney damage due to diabetes) and congestive heart failure. Towards this direction, our study has been focused on the discovery of novel agents bearing different scaffolds which may evolve as a new class of AT₁ receptor antagonists. To fulfill this aim, a combination of computational approaches and biological assays were implemented. Particularly, a pharmacophore model was established and served as a 3D search query to screen the ChEMBL15 database. The reliability and accuracy of virtual screening results were improved by using molecular docking studies. In total, 4 compounds with completely diverse chemical scaffolds from potential ARBs, were picked and tested for their binding affinity to AT₁ receptor. Results revealed high nanomolar to micromolar affinity (IC₅₀) for all the compounds. Especially, compound 4 exhibited a binding affinity of 199 nM. Molecular dynamics simulations were utilized in an effort to provide a molecular basis of their binding to AT₁R in accordance to their biological activities.     

Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors

Pharmacophore models of Polo-like kinase-1 (PLK1) inhibitors have been established by using the HipHop and HypoGen algorithms implemented in the Catalyst software package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9895), consists of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic feature, and one hydrophobic aliphatic feature. Hypo1 was further validated by test set and cross validation method. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge, and Chinese Nature Product Database (CNPD). The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking study to refine the retrieved hits and as a result to reduce the rate of false positive. Finally, a total of 20 compounds were selected and have been shifted to in vitro and in vivo studies. As far as we know, this is the first report on the pharmacophore modeling even the first publicly reported virtual screening study of PLK1 inhibitors.     

The adenosine A2A antagonistic properties of selected C8-substituted xanthines

The adenosine A_2A receptor is considered to be an important target for the development of new therapies for Parkinson’s disease. Several antagonists of the A_2A receptor have entered clinical trials for this purpose and many research groups have initiated programs to develop A_2A receptor antagonists. Most A_2A receptor antagonists belong to two different chemical classes, the xanthine derivatives and the amino-substituted heterocyclic compounds. In an attempt to discover high affinity A_2A receptor antagonists and to further explore the structure–activity relationships (SARs) of A_2A antagonism by the xanthine class of compounds, this study examines the A_2A antagonistic properties of series of (E)-8-styrylxanthines, 8-(phenoxymethyl)xanthines and 8-(3-phenylpropyl)xanthines. The results document that among these series, the (E)-8-styrylxanthines have the highest binding affinities with the most potent homologue, (E)-1,3-diethyl-7-methyl-8-[(3-trifluoromethyl)styryl]xanthine, exhibiting a K_i value of 11.9 nM. This compound was also effective in reversing haloperidol-induced catalepsy in rats, providing evidence that it is in fact an A_2A receptor antagonist. The importance of substitution at C8 with the styryl moiety was demonstrated by the finding that none of the 8-(phenoxymethyl)xanthines and 8-(3-phenylpropyl)xanthines exhibited high binding affinities for the A_2A receptor.     

Identification of potent virtual leads to design novel PLK1 inhibitors: pharmacophore modelling,virtual screening and molecular docking studies

The aim of this study was to identify novel scaffolds and utilise them in designing potent PLK1 inhibitors. Three-dimensional pharmacophore models on the basis of chemical features were developed for PLK1 on the basis of the known inhibitors. The best pharmacophore model, Hypo 1, which has the highest correlation (0.96), the highest cost difference (75.7494), the lowest total cost and RMSD (75.7494, 0.5458), contains two hydrophobics, one ring aromatic and one hydrogen donor. Hypo 1 was validated by the test set, decoy set and the Fischer's randomisation method. Then it was used for chemical database virtual screening. The hit compounds were filtered by Lipinski's rule of five and absorption, distribution, metabolism, elimination and toxicity properties. Finally, 24 compounds with good estimated activity values were used for docking studies. These results will be used to develop new inhibitors of PLK1 as leads.     

Design,synthesis and structure–activity relationship of novel quinoxalin-2-carboxamides as 5-HT3 receptor antagonists for the management of depression

A novel series of quinoxalin-2-carboxamides were designed based on the ligand-based approach, employing a three-point pharmacophore model; it consists of an aromatic residue and a linking carbonyl group and a basic nitrogen. The target new chemical entities were synthesized from the key intermediate, quinoxalin-2-carboxylic acid, by coupling it with various amines in the presence of 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC·HCl) and 1-hydroxybenzotriazole (HOBt). The obtained compounds’ structures were confirmed by spectral data. The target new chemical entities were evaluated for their 5-HT₃ receptor antagonisms in longitudinal muscle myenteric plexus preparation from guinea pig ileum against 5-HT₃ agonist, 2-methyl-5-HT, which was expressed in the form of pA₂ value. All the synthesized compounds showed antagonism towards 5-HT₃ receptor; based on this result, a structure–activity relationship was derived, which reveals that the aromatic residue in 5-HT₃ receptor antagonists may have hydrophobic interaction with 5-HT₃ receptor. Regardless of their antagonistic potentials, all the synthesized molecules were screened for their anti-depressant potentials by using forced swim test in mice model; interestingly none of the tested compounds affect the locomotion of mice in the tested dose levels. Compounds with significant pA₂ values exhibited good anti-depressant-like activity as compared to the vehicle-treated group.     

3D pharmacophore models for thromboxane A2 receptor antagonists

Thromboxane A₂ (TXA₂) is an endogenous arachidonic acid derivative closely correlated to thrombosis and other cardiovascular diseases. The action of TXA₂ can be effectively inhibited with TXA₂ receptor antagonists (TXRAs). Previous studies have attempted to describe the interactions between the TXA₂ receptor and its ligands, but their conclusions are still controversial. In this study, ligand-based computational drug design is used as a new and effective way to investigate the structure-activity relationship of TXRAs. Three-dimensional pharmacophore models of TXRAs were built with HypoGenRefine and HipHop modules in CATALYST software. The optimal HypoGenRefine model was developed on the basis of 25 TXRAs. It consists of two hydrophobic groups, one aromatic ring, one hydrogen-bond acceptor and four excluded volumes. The optimal HipHop model contains two hydrophobic groups and two hydrogen-bond acceptors. These models describe the key structure-activity relationship of TXRAs, can predict their activities, and can thus be used to design novel antagonists. Figure Optimal three-dimensional pharmacophore models of TXA₂ receptor antagonists (TXRAs) built with HypoGenRefine (a) and HipHop (b) modules. a Hypo-1 model mapped with compounds 11 (purple), and 20 (green). b Hypo-2 model mapped with compounds 31 (green) and 64 (yellow). Spheres: Green Hydrogen bond acceptors (HBA), cyanhydrophobic (H), orange aromatic rings (RA), black excluded volumes (EV) Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

3D QSAR pharmacophore-based virtual screening for the identification of potential inhibitors of tyrosinase

Abstract

Tyrosinase plays an important role in melanin biosynthesis and protects skin against ultraviolet radiations. Functional deficiency of tyrosinase results in serious dermatological diseases. Tyrosinase also participates in neuromelanin formation in the human brain, which leads to neurodegeneration resulting in Parkinson’s disease. In fruits and vegetables, tyrosinase plays a critical role in senescence, causing undesired browning that results in faster deterioration and shorter shelf lines. The only commercially available tyrosinase is mushroom tyrosinase and it shows the highest homology to the mammalian tyrosinase. Although kojic acid is currently used as a tyrosinase inhibitor, they have serious side effects such as dermatitis, carcinogenesis and hepatotoxicity. Therefore, in order to develop a more active and safer tyrosinase inhibitor, 3D QSAR pharmacophore models were generated based on experimentally known inhibitors. The pharmacophore model, Hypo1, was developed with a large cost difference, high correlation coefficient and low RMS deviation. Hypo1 showed a good spatial arrangement; consisting of five-point features including two hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic features. Hypo1 was further validated by cost analysis, test set and Fisher’s randomisation method. Hypo1 was used as a 3D query for screening the in-house drug-like databases, and the hits were further selected by applying ADMET, Lipinski’s rule of five and fit value criteria. To identify binding conformations, the obtained hits were subjected to molecular docking. Finally, molecular dynamics simulations revealed the appropriate binding modes of hit compounds. To conclude, we propose the final three hit compounds with new structural scaffolds as a virtual candidate as tyrosinase inhibitors.

Communicated by Ramaswamy H. Sarma