The emergence of the highly pathogenic avian influenza (HPAI) H5N1 virus and the recent global circulation of H1N1 swine-origin influenza virus in 2009 have highlighted the need for new ...anti-influenza therapies. This has been made all the more important with the emergence of antiviral-resistant strains. Recent progress in achieving three-dimensional (3D) crystal structures of influenza viral proteins and efficient tools available for pharmacophore-based virtual screening are aiding us in the discovery and design of new antiviral compounds.
This review discusses pharmacophore modeling as a potential cost-effective and time-saving technology for new drug discovery as an alternative to high-throughput screening. Based on this technical platform, the authors discuss current progress and future prospects for developing novel influenza antivirals against pre-existing or emerging novel targets.
Although it might be at an infant stage of development, the availability of the 3D crystal structures of influenza viral proteins is expected to accelerate the application of structure-based drug design (SBDD) and pharmacophore modeling. Furthermore, the neuraminidase inhibitor, one of the most successful examples of a SBDD, still receives great attention because of its superb antiviral activities and the resistance of influenza strains to oseltamivir. However, despite much success, pharmacophore-based virtual screening exhibits limited predictive power in hit identification. Further improvements in pharmacophore detection algorithms, proper combinations of in silico methods as well as judicious choosing of compounds are expected to improve the hit rate. With the help of these technologies, the discovery of anti-influenza agents will be accelerated.
Tuberculosis continues to be a major cause of mortality worldwide despite significant advances in chemotherapy and development of the BCG vaccine. Although curable, the tuberculosis treatment period ...(6-9 months) presents many concerns, including patient noncompliance and the development of drug toxicity and drug resistance. This study aimed to understand the protein-protein interactions of key proteins involved in the Mycobacterium tuberculosis STPK signal transduction pathway (such as PknB, PknE, and PstP); in addition, we attempted to identify promising leads for the inhibition of protein-protein interactions. Interactome analyses revealed the interactions of these protein targets with several other proteins, including PknG and PbpA. Drug-like candidates were screened based on Lipinski's rule of five and the absorption digestion metabolism excretion toxicity. Molecular docking of the target proteins with the selected ligands identified cryptolepine HCl to be a common molecule interacting with all protein targets (with a good docking score). The generation of a pharmacophore model for cryptolepine HCl revealed three pharmacophoric regions: aromatic hydrocarbon, hydrogen bond acceptor, and hydrogen bond donor, which play important roles in its interaction with the protein targets. Therefore, cryptolepine HCl appears to be a promising drug candidate for further optimization and validation against M. tuberculosis.
Cytochrome p450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. As only 6 isoenzymes are responsible for approximately 90 % of known oxidative drug metabolism, a number ...of frequently prescribed drugs share the CYP-mediated metabolic pathways. Competing for a single enzyme by the co-administered therapeutic agents can substantially alter the plasma concentration and clearance of the agents. Furthermore, many drugs are known to inhibit certain p450 enzymes which they are not substrates for. Because some drug-drug interactions could cause serious adverse events leading to a costly failure of drug development, early detection of potential drug-drug interactions is highly desirable. The ultimate goal is to be able to predict the CYP specificity and the interactions for a novel compound from its chemical structure. Current computational modeling approaches, such as two-dimensional and three-dimensional quantitative structure-activity relationship (QSAR), pharmacophore mapping and machine learning methods have resulted in statistically valid predictions. Homology models have been often combined with 3D-QSAR models to impose additional steric restrictions and/or to identify the interaction site on the proteins. This article summarizes the available models, methods, and key findings for CYP1A2, 2A6, 2C9, 2D6 and 3A4 isoenzymes.
The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed ...virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an
in-house
developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.
Development of fatty acid synthase (FAS) inhibitors has increasingly attracted much attention in recent years due to their potential therapeutic use in obesity and cancers. In this investigation, ...pharmacophore modeling based on the first crystal structure of human KS domain of FAS was carried out. The established pharmacophore model was taken as a 3D query for retrieving potent FAS inhibitors from the chemical database Specs. Docking study was further carried out to refine the obtained hit compounds. Finally, a total of 28 compounds were selected based on the ranking order and visual examination, which were first evaluated by a cell line-based assay. Seven compounds that have good inhibition activity against two FAS overexpressing cancer cell lines were further evaluated by an enzyme-based assay. One compound with a new chemical scaffold was found to have low micromolar inhibition potency against FAS, which has been subjected to further chemical structural modification.
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▶ A H
2 receptor agonist pharmacophore model was built. ▶ A homology model was built for H
2 receptor. ▶ The models were validated by diverse methods and they were consistent with ...each other. ▶ The ‘induced’ model can dramatically improve the enrichment factor.
Type 2 histamine receptor (H
2R) is widely distributed in the body. Its main function is modulating the secretion of gastric acid. Most gastric acid-related diseases are closely associated with it. In this study, a combination of pharmacophore modeling, homology modeling, molecular docking and molecular dynamics methods were performed on human H
2R and its agonists to investigate interaction details between them. At first, a pharmacophore model of H
2R agonists was developed, which was then validated by QSAR and database searching. Afterwards, a model of the H
2R was built utilizing homology modeling method. Then, a reference agonist was docked into the receptor model by induced fit docking. The ‘induced’ model can dramatically improve the recovery ratio from 46.8% to 69.5% among top 10% of the ranked database in the simulated virtual screening. The pharmocophore model and the receptor model matched very well each other, which provided valuable information for future studies. Asp98, Asp186 and Tyr190 played key roles in the binding of H
2R agonists, and direct interactions were observed between the three residues and agonists. Residue Tyr250 could also form a hydrogen bond with H
2R agonists. These findings would be very useful for the discovery of novel and potent H
2R agonists.
A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and ...multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential
(
r
=
0.87
,
F
-statistic
=
69.13
,
r
BS
2
=
0.76
,
r
LOO
2
=
0.68
)
. The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.
Pharmacophoric features of the binding models Hypo 4/15 and Hypo9/12 (HBD as violet vectored spheres and Hbic as blue spheres, PosIon as red spheres, RingArom as orange vectored spheres, and ...exclusion volumes as gray spheres), fitted against the most active hit 120 (IC50=1.43 μM, Table 4).
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•Pharmacophoric space of 115 (HER2/ErbB2) inhibitors was explored.•Two pharmacophore models, Hypo4/15 and Hypo9/12, illustrated the best performance.•In silico screening of NCI and DrugBank databases identified 80 potential HER2 inhibitors.•4 of investigated hits selectively inhibited the growth of SKOV3 ovarian cells with IC50<5μM.
To discover potential antitumor agents directed toward human epidermal growth factor receptor-2HER2/ErbB2 overexpression in cancer, we have explored the pharmacophoric space of 115 HER2/ErbB2 inhibitors. This identified 240 pharmacophores which were subsequently clustered into 20 groups and cluster centers were used as 3D-pharmacophoric descriptors in QSAR analysis with 2D-physicochemical descriptors to select the optimal combination. We were obliged to use ligand efficiency as the response variable because the logarithmic transformation of bioactivities failed to access self-consistent QSAR models. Two binding pharmacophore models emerged in the optimal QSAR equation, suggesting the existence of distinct binding modes accessible to ligands within the HER2/ErbB2 binding pocket. The QSAR equation and its associated pharmacophore models were employed to screen the National Cancer Institute (NCI) and Drug Bank databases to search for new, promising, and structurally diverse HER2 inhibitory leads. Inhibitory activities were tested against HER2-overexpressing SKOV3 Ovarian cancer cell line and MCF-7 which express low levels of HER2. In silico mining identified 80 inhibitors out of which four HER2 selective compounds inhibited the growth of SKOV3 cells with IC50 values < 5μM and with virtually no effect in MCF-7 cells. These lead compounds are excellent candidates for further optimization.
PKC-βII is a conventional isoform of PKC. It is overexpressed in hyperglycemic conditions and is known to trigger various diabetic complications, mainly cardiovascular complications and to a certain ...extent nephropathy, neuropathy, retinopathy etc. Selective inhibition of this enzyme will be one of the favorable approaches to treat diabetes-mellitus-related complications. Due to high sequence similarities among PKC isoforms, selective inhibition of PKC-βII is difficult and yet to be achieved successfully.
This review discusses the studies carried out in various aspects of PKC-βII. The biological aspects, crystal structure data, structure–activity relationship study (SAR) and in silico studies related to PKC-βII such as homology modeling, molecular docking, molecular dynamics, quantitative structure–activity relationship (QSAR) studies and pharmacophore modeling etc. are summarized.
PKC-βII is a potential target for treating diabetes-related complications. Selective inhibitors of this enzyme are under clinical trials but to date, success has not been achieved. Thus, extensive research is essential in this direction; the contribution of in silico tools in designing and optimizing selective inhibitors of PKC-βII is valuable.
The present study describes the development of 3D-QSAR studies on angiotensin II receptor based on the selected pharmacophore model. A four-point pharmacophore with one hydrogen bond acceptor (A), ...one hydrophobic (H), and two aromatic ring features (R) as pharmacophore features was developed by PHASE module. The pharmacophore hypothesis yielded a statistically significant model with good partial least-squares results. Thus, obtained 3D-QSAR model with partial least-squares (PLS) factor regression coefficient value (
r
2
= 0.9547) for training set and high value of cross-validated correlation coefficient
Q
2
= 0.7418, root-mean-squared error RMSE = 0.1257. This model was found to yield reliable clues for the further optimization of benzimidazole derivatives in the dataset, which plays an important role in molecular drug design approach.
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