Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors

Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.     

Neuraminidase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 22 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity, for discovering new potent neuraminidase (NA) inhibitors to fight against avian influenza virus. The best hypothesis (Hypo1), consisting of five features, namely, one positive ionizable group, one negative ionizable group, one hydrophobic point, and two hydrogen-bond donors, has a correlation coefficient of 0.902, a root mean square deviation of 1.392, and a cost difference of 72.88, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 88 known NA inhibitors in our test set with a correlation coefficient of 0.818 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.     

Farnesyltransferase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity as required by HypoGen program in the Catalyst software, for discovering new farnesyltransferase (FTase) inhibitors. The best hypothesis (Hypo1), consisting of four features, namely, two hydrogen-bond acceptors, one hydrophobic point, and one ring aromatic feature, has a correlation coefficient of 0.949, a root-mean-square deviation of 1.321, and a cost difference of 163.15, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 227 known FTase inhibitors in our test set with a correlation coefficient of 0.776 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.     

Pharmacophore identification of KSP inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available KSP (kinesin spindle protein) inhibitors in Catalyst software package. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group), has a correlation coefficient of 0.965. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.     

Pharmacophore identification of Raf-1 kinase inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available Raf-1 kinase inhibitors. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic groups), has a correlation coefficient of 0.972. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.     

Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using Catalyst

Cyclooxygenase (COX) enzymes catalyse the biosynthesis of prostaglandins and thromboxane from arachidonic acid (AA). We summarize in this paper, the development of pharmacophores of a dataset of inhibitors for COX-2 by using the Catalyst/Hypogen module using six chemically diverse series of compounds. Training set consisting of 24 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 10000 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, one hydrogen bond donor, one hydrogen bond acceptor, one hydrophobic aliphatic and one ring aromatic feature, had a correlation (r) of 0.954 and a root mean square deviation of 0.894. The entropy (configuration cost) value of the hypotheses was 16.79, within the allowed range. The difference between the null hypothesis and the fixed cost and between the null hypothesis and the total cost of the best hypothesis (hypothesis 1) was 88.37 and 78.51, respectively. The model was validated on a test set consisting of six different series of structurally diverse 22 compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug like molecules to retrieve new chemical entities as potent COX-2 inhibitors. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis. Figure 3D Pharmacophore model generated using structurally diverse COX-2 inhibitors     

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.     

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.     

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.     

Novel virtual lead identification in the discovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation

High level of hematopoietic cell kinase (Hck) is associated with drug resistance in chronic myeloid leukemia. Additionally, Hck activity has also been connected with the pathogenesis of HIV-1 and chronic obstructive pulmonary disease. In this study, three-dimensional (3D) QSAR pharmacophore models were generated for Hck based on experimentally known inhibitors. A best pharmacophore model, Hypo1, was developed with high correlation coefficient (0.975), Low RMS deviation (0.60) and large cost difference (49.31), containing three ring aromatic and one hydrophobic aliphatic feature. It was further validated by the test set (r?=?0.96) and Fisher’s randomization method (95%). Hypo 1 was used as a 3D query for screening the chemical databases, and the hits were further screened by applying Lipinski’s rule of five and ADMET properties. Selected hit compounds were subjected to molecular docking to identify binding conformations in the active site. Finally, the appropriate binding modes of final hit compounds were revealed by molecular dynamics (MD) simulations and free energy calculation studies. Hence, we propose the final three hit compounds as virtual candidates for Hck inhibitors.     

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 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.     

Pharmacophore modeling, molecular docking, and molecular dynamics simulation approaches for identifying new lead compounds for inhibiting aldose reductase 2

Aldose reductase 2 (ALR2), which catalyzes the reduction of glucose to sorbitol using NADP as a cofactor, has been implicated in the etiology of secondary complications of diabetes. A pharmacophore model, Hypo1, was built based on 26 compounds with known ALR2-inhibiting activity values. Hypo1 contains important chemical features required for an ALR2 inhibitor, and demonstrates good predictive ability by having a high correlation coefficient (0.95) as well as the highest cost difference (128.44) and the lowest RMS deviation (1.02) among the ten pharmacophore models examined. Hypo1 was further validated by Fisher's randomization method (95%), test set (r = 0.91), and the decoy set shows the goodness of fit (0.70). Furthermore, during virtual screening, Hypo1 was used as a 3D query to screen the NCI database, and the hit leads were sorted by applying Lipinski's rule of five and ADME properties. The best-fitting leads were subjected to docking to identify a suitable orientation at the ALR2 active site. The molecule that showed the strongest interactions with the critical amino acids was used in molecular dynamics simulations to calculate its binding affinity to the candidate molecules. Thus, Hypo1 describes the key structure-activity relationship along with the estimated activities of ALR2 inhibitors. The hit molecules were searched against PubChem to find similar molecules with new scaffolds. Finally, four molecules were found to satisfy all of the chemical features and the geometric constraints of Hypo1, as well as to show good dock scores, PLPs and PMFs. Thus, we believe that Hypo1 facilitates the selection of novel scaffolds for ALR2, allowing new classes of ALR2 inhibitors to be designed.     

Pharmacophore modeling of human adenosine receptor A2A antagonists

Three-dimensional pharmacophore models of human adenosine receptor A_2A antagonists were developed based on 23 diverse compounds selected from a large number of A_2A antagonists. The best pharmacophore model, Hypo1, contained five features: one hydrogen bond donor , three hydrophobic points and one ring aromatic. Its correlation coefficient, root mean square deviation, and cost difference values were 0.955, 0.921 and 84.4, respectively, suggested that the Hypo1 model was reasonable and reliable. This model was validated by three methods: a test set of 106 diverse compounds, a simulated virtual screening, and superimposition with the crystal structure of A_2A receptor. The results showed that Hypo1 was not only in agreement with the A_2A crystal structure and literature reports, but also well identified active A_2A antagonists from the virtual database. This methodology provides helpful information and a robust tool for the discovery of potent A_2A antagonists.     

Design checkpoint kinase 2 inhibitors by pharmacophore modeling and virtual screening techniques

Damage to DNA is caused by ionizing radiation, genotoxic chemicals or collapsed replication forks. When DNA is damaged or cells fail to respond, a mutation that is associated with breast or ovarian cancer may occur. Mammalian cells control and stabilize the genome using a cell cycle checkpoint to prevent damage to DNA or to repair damaged DNA. Checkpoint kinase 2 (Chk2) is one of the important kinases, which strongly affects DNA-damage and plays an important role in the response to the breakage of DNA double-strands and related lesions. Therefore, this study concerns Chk2. Its purpose is to find potential inhibitors using the pharmacophore hypotheses (PhModels) and virtual screening techniques. PhModels can identify inhibitors with high biological activities and virtual screening techniques are used to screen the database of the National Cancer Institute (NCI) to retrieve compounds that exhibit all of the pharmacophoric features of potential inhibitors with high interaction energy. Ten PhModels were generated using the HypoGen best algorithm. The established PhModel, Hypo01, was evaluated by performing a cost function analysis of its correlation coefficient (r), root mean square deviation (RMSD), cost difference, and configuration cost, with the values 0.955, 1.28, 192.51, and 16.07, respectively. The result of Fischer’s cross-validation test for the Hypo01 model yielded a 95% confidence level, and the correlation coefficient of the testing set (r_test) had a best value of 0.81. The potential inhibitors were then chosen from the NCI database by Hypo01 model screening and molecular docking using the cdocker docking program. Finally, the selected compounds exhibited the identified pharmacophoric features and had a high interaction energy between the ligand and the receptor. Eighty-three potential inhibitors for Chk2 are retrieved for further study.     

Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies

Chemical feature based pharmacophore models were generated for Toll-like receptors 7 (TLR7) agonists using HypoGen algorithm, which is implemented in the Discovery Studio software. Several methods tools used in validation of pharmacophore model were presented. The first hypothesis Hypo1 was considered to be the best pharmacophore model, which consists of four features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features. In addition, homology modeling and molecular docking studies were employed to probe the intermolecular interactions between TLR7 and its agonists. The results further confirmed the reliability of the pharmacophore model. The obtained pharmacophore model (Hypo1) was then employed as a query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit was identified as a potent TLR7 agonist, which has antiviral activity against hepatitis virus in vitro. Therefore, our current work provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 agonists with desired biological activity.     

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.     

Three-dimensional pharmacophore hypotheses for the locust neuronal octopamine receptor (OAR3). Part 2: agonists.

Three-dimensional pharmacophore hypotheses were built from a set of 43 agonists against octopamine receptor class 3 (OAR3) in locust nervous tissue. Among the 10 chemical-featured models generated by program Catalyst/Hypo, a hypothesis including hydrogen-bond acceptor (HBA), hydrophobic (Hp), and hydrophobic aliphatic (HpA1) features was considered to be important and predictive in evaluating OAR3 agonists. While the ideal and null hypotheses had a cost of 156.40 and 239.20, respectively, the 10 resulting hypotheses possessed costs from 169.89 to 175.81. The best hypothesis that was confirmed to have a 95% chance of true correlation yielded a low RMS of 0.757 and high regression r of 0.933. Active agonists mapped well onto all the features of the hypothesis such as HBA, Hp, and HpA1. On the other hand, inactive compounds were shown to be difficult to achieve the energetically favorable conformation which is found in the active molecules in order to fit the 3-D chemical feature pharmacophore models.     

Identification of Novel HIV 1- Protease Inhibitors: Application of Ligand and Structure Based Pharmacophore Mapping and Virtual Screening

A combined ligand and structure-based drug design approach provides a synergistic advantage over either methods performed individually. Present work bestows a good assembly of ligand and structure-based pharmacophore generation concept. Ligand-oriented study was accomplished by employing the HypoGen module of Catalyst in which we have translated the experimental findings into 3-D pharmacophore models by identifying key features (four point pharmacophore) necessary for interaction of the inhibitors with the active site of HIV-1 protease enzyme using a training set of 33 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond acceptors and two hydrophobic, showed a correlation (r) of 0.90 and a root mean square of 0.71 and cost difference of 56.59 bits between null cost and fixed cost. The model was validated using CatScramble technique, internal and external test set prediction. In the second phase of our study, a structure-based five feature pharmacophore hypothesis was generated which signifies the importance of hydrogen bond donor, hydrogen bond acceptors and hydrophobic interaction between the HIV-1 protease enzyme and its inhibitors. This work has taken a significant step towards the full integration of ligand and structure-based drug design methodologies as pharmacophoric features retrieved from structure-based strategy complemented the features from ligand-based study hence proving the accuracy of the developed models. The ligand-based pharmacophore model was used in virtual screening of Maybridge and NCI compound database resulting in the identification of four structurally diverse druggable compounds with nM activities.     

Pharmacophore modeling and in silico screening for new KDR kinase inhibitors

In order to elucidate the essential structural features for KDR kinase inhibitors, three-dimensional pharmacophore hypotheses were built on the basis of a set of known KDR kinase inhibitors selected from the literature with CATALYST program. Several methods tools used in validation of pharmacophore hypothsis were presented, and the first hypothesis (Hypo1) was considered to be the best pharmacophore hypothesis. The model (Hypo1) was then employed as 3D search query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit illustrated high binding affinity with KDR kinase measured by the surface plasmon resonance biosensor. Docking studies may help elucidate the mechanisms of KDR kinase receptor-ligand interactions.