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.     

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.     

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     

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.     

Discover potential inhibitors for PFKFB3 using 3D-QSAR,virtual screening,molecular docking and molecular dynamics simulation

Abstract

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40?ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.     

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.     

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.     

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.     

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.     

Pharmacophore modeling study based on known Spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors

In this investigation, chemical features based 3D pharmacophore models were developed based on the known inhibitors of Spleen tyrosine kinase (Syk) with the aid of hiphop and hyporefine modules within catalyst. The best quantitative pharmacophore model, Hypo1, was used as a 3D structural query for retrieving potential inhibitors from chemical 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 studies to refine the retrieved hits. Finally 30 compounds were selected from the top ranked hit compounds and conducted an in vitro kinase inhibitory assay. Six compounds showed a good inhibitory potency against Syk, which have been selected for further investigation.     

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.     

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.     

Exploration of Novel Inhibitors for Bruton’s Tyrosine Kinase by 3D QSAR Modeling and Molecular Dynamics Simulation

Bruton’s tyrosine kinase (BTK) is a cytoplasmic, non-receptor tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in many cellular signaling pathways. B cell malignancies are dependent on BCR signaling, thus making BTK an efficient therapeutic target. Over the last few years, significant efforts have been made in order to develop BTK inhibitors to treat B-cell malignancies, and autoimmunity or allergy/hypersensitivity but limited success has been achieved. Here in this study, 3D QSAR pharmacophore models were generated for Btk based on known IC₅₀ values and experimental energy scores with extensive validations. The five features pharmacophore model, Hypo1, includes one hydrogen bond acceptor lipid, one hydrogen bond donor, and three hydrophobic features, which has the highest correlation coefficient (0.98), cost difference (112.87), and low RMS (1.68). It was further validated by the Fisher’s randomization method and test set. The well validated Hypo1 was used as a 3D query to search novel Btk inhibitors with different chemical scaffold using high throughput virtual screening technique. The screened compounds were further sorted by applying ADMET properties, Lipinski’s rule of five and molecular docking studies to refine the retrieved hits. Furthermore, molecular dynamic simulation was employed to study the stability of docked conformation and to investigate the binding interactions in detail. Several important hydrogen bonds with Btk were revealed, which includes the gatekeeper residues Glu475 and Met 477 at the hinge region. Overall, this study suggests that the proposed hits may be more effective inhibitors for cancer and autoimmune therapy.     

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     

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.     

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.     

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.     

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.     

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.