Pharmacophore mapping of a series of pyrrolopyrimidines,indolopyrimidines and their congeners as multidrug-resistance-associated protein (MRP1) modulators

Pharmacophore mapping studies were undertaken for a series of molecules belonging to pyrrolopyrimidines, indolopyrimidines and their congeners as multidrug resistance-associated protein (MRP1) modulators. A five-point pharmacophore with two hydrogen bond acceptors (A), one lipophilic/hydrophobic group (H), one positive ionic feature (P) and one aromatic ring (R) as pharmacophoric features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r ² = 0.799 for training set molecules. The model generated showed excellent predictive power, with a correlation coefficient Q ² = 0.679 for an external test set of 20 molecules. The pharmacophore was further validated using four structurally diverse compounds with MRP1 modulatory activity. These compounds mapped well onto four of the five features of the pharmacophore. The pharmacophore proposed here was then utilised for the successful retrieval of active molecules with diverse chemotypes from database search. The geometry and features of pharmacophore are expected to be useful for the design of selective MRP1 inhibitors. Figure Alignment of multidrug resistance-associated protein (MRP1) inhibitors with the developed pharmacophore. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Pharmacophore models generation by catalyst and phase consensus-based virtual screening protocol against PI3Kα inhibitors

Phosphoinositide 3-kinases (PI3Ks) family has emerged as promising targets for novel therapeutic agents against neoplastic diseases. Pharmacophore and 3D-quantitative structure–activity relationship modelling were applied to study the structure–activity relationship of PI3K inhibitors. The best HypoGen pharmacophore hypothesis Hypo1 with a correlation coefficient of 0.961 consists of one hydrogen-bond acceptor, one hydrogen-bond donor and two hydrophobic features, whereas the best phase hypothesis AADRRR.378 with favourable statistics (q² = 0.7368, r² = 0.9863) has two hydrogen-bond acceptors, one hydrogen-bond donor and three ring aromatic features. Multiple methods, such as Fischer validation, molecular docking and mapping of test set molecules, were carried out to validate these pharmacophore models. Furthermore, a comparative molecular similarity indices analysis candidate hypothesis model was generated as a supplement of pharmacophore hypothesis. Detailed protein–ligand binding information obtained by Glide was utilised in compound optimisation and virtual screening. A molecular database of 133 known inhibitors and 6179 decoys was built for a screening test to quantitatively analyse various hypotheses and scoring parameters. Finally, we designed a workflow integrating HypoGen pharmacophore searching, phase pharmacophore searching and molecular docking for screening the database. With an improved criterion of enrichment factor (EF = 17.43) and ROC curve (AUC = 0.946), this workflow would provide us an original method for novel PI3K inhibitors.     

Stepwise development of structure–activity relationship of diverse PARP-1 inhibitors through comparative and validated in silico modeling techniques and molecular dynamics simulation

Inhibitors of poly (ADP-ribose) polymerase-1 (PARP-1) enzyme are useful for the treatment of various diseases including cancer. Comparative in silico studies were performed on different ligand-based (2D-QSAR, Kernel-based partial least square (KPLS) analysis, Pharmacophore Search Engine (PHASE) pharmacophore mapping), and structure-based (molecular docking, MM-GBSA analyses, Gaussian-based 3D-QSAR analyses on docked poses) modeling techniques to explore the structure–activity relationship of a diverse set of PARP-1 inhibitors. Two-dimensional (2D)-QSAR highlighted the importance of charge topological index (JGI7), fractional polar surface area (Jurs_FPSA3), and connectivity index (CIC2) along with different molecular fragments. Favorable and unfavorable fingerprints were demonstrated in KPLS analysis, whereas important pharmacophore features (one acceptor, one donor, and two ring aromatic) along with favorable and unfavorable field effects were demonstrated in PHASE-based pharmacophore model. MM-GBSA analyses revealed significance of different polar, non-polar, and solvation energies. Docking-based alignment of ligands was used to perform Gaussian-based 3D-QSAR study that further demonstrated importance of different field effects. Overall, it was found that polar interactions (hydrogen bonding, bridged hydrogen bonding, and pi–cation) play major roles for higher activity. Steric groups increase the total contact surface area but it should have higher fractional polar surface area to adjust solvation energy. Structure-based pharmacophore mapping spotted the positive ionizable feature of ligands as the most important feature for discriminating highly active compounds from inactives. Molecular dynamics simulation, conducted on highly active ligands, described the dynamic behaviors of the protein complexes and supported the interpretations obtained from other modeling analyses. The current study may be useful for designing PARP-1 inhibitors.     

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.     

Automated generation of MCSS-derived pharmacophoric DOCK site points for searching multiconformation databases

All docking methods employ some sort of heuristic to orient the ligand molecules into the binding site of the target structure. An automated method, MCSS2SPTS, for generating chemically labeled site points for docking is presented. MCSS2SPTS employs the program Multiple Copy Simultaneous Search (MCSS) to determine target-based theoretical pharmacophores. More specifically, chemically labeled site points are automatically extracted from selected low-energy functional-group minima and clustered together. These pharmacophoric site points can then be directly matched to the pharmacophoric features of database molecules with the use of either DOCK or PhDOCK to place the small molecules into the binding site. Several examples of the ability of MCSS2SPTS to reproduce the three-dimensional pharmacophoric features of ligands from known ligand-protein complex structures are discussed. In addition, a site-point set calculated for one human immunodeficiency virus 1 (HIV1) protease structure is used with PhDOCK to dock a set of HIV1 protease ligands; the docked poses are compared to the corresponding complex structures of the ligands. Finally, the use of an MCSS2SPTS-derived site-point set for acyl carrier protein synthase is compared to the use of atomic positions from a bound ligand as site points for a large-scale DOCK search. In general, MCSS2SPTS-generated site points focus the search on the more relevant areas and thereby allow for more effective sampling of the target site.     

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.     

A minimalistic 3D pharmacophore model for cyclopentapeptide CXCR4 antagonists

Because of its involvement in HIV entry, the chemokine receptor CXCR4 is an attractive target for antiretroviral drugs. Despite the large number of CXCR4 inhibitors studied, the 3D pharmacophore for binding to CXCR4 remains elusive, mainly as a result of conformational flexibility inherent in the identified ligands. In the present study, an exhaustive systematic exploration of the conformational space for a series of analogs of FC131, a cyclopentapeptide CXCR4 antagonist, has been performed. By comparing the resulting low-energy conformations using different sets of atoms, specific conformational features common only to the high/medium affinity compounds were identified. These features included the spatial arrangement of three pharmacophoric side chains as well as the orientation of a specific backbone amide bond. Together these features represent a minimalistic 3D pharmacophore model for binding of the cyclopentapeptide antagonists to CXCR4. The model enables rationalization of the experimental affinity data for this class of compounds as well as for the peptidomimetic KRH-1636.     

Multidimensional screening and design of pharmaceuticals by using endocrine pharmacophores.

A novel computational technology derived from gene structure has been developed for screening, selecting, and designing pharmaceutical candidates. Pharmacophores, or three-dimensional molecular blueprints, were created by docking known active structures into specific sites in partially unwound DNA. The pharmacophores are composites of the van der Waals surfaces and hydrogen bonding functional groups of active molecules. Once created, molecules can be inserted into the pharmacophores and degree of fit quantitated by the volume of the molecule that fits within the composite surface and the magnitude of electrostatic interactions with charged atoms on the pharmacophore. Here, we describe endocrine pharmacophores and in particular the estrogen pharmacophore derived by docking active ligands into partially unwound DNA. Fit of candidate structures into the estrogen pharmacophore correlated with estrogenic (uterotropic) activity. For example, the super active estrogens moxestrol and 11beta-acetoxyestradiol fit better within the site than estradiol. Bisphenol A, a putative endocrine disrupter with suspected estrogenic activity, was a poor fit in the pharmacophore. Consistent with this prediction, bisphenol A was recently shown to lack uterotropic activity. The capacity of the endocrine pharmacophores to predict certain nontarget activities was demonstrated by using the antiandrogen cyproterone acetate that did not fit the estrogen or thyroid pharmacophores but fit partially into the progestin and glucocorticoid pharmacophores. Cyproterone acetate has been reported to have weak progestational and glucocorticoid activities. The pharmacophores provide for the first time a multidimensional computational method that can simultaneously predict multiple activities of diverse molecular structures.     

Pharmacophore-based virtual screening approach for identification of potent natural modulatory compounds of human Toll-like receptor 7

Abstract

Toll-like receptor 7 (TLR7) is a transmembrane glycoprotein playing very crucial role in the signaling pathways involved in innate immunity and has been demonstrated to be useful in fighting against infectious disease by recognizing viral ssRNA & specific small molecule agonists. In order to find novel human TLR7 (hTLR7) modulators, computational ligand-based pharmacophore modeling approach was used to identify the molecular chemical features required for the modulation of hTLR7 protein. A training set of 20 TLR7 agonists with their known experimental activity was used to create pharmacophore model using 3D-QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best developed hypothesis consists of four pharmacophoric features namely, one hydrogen bond donor (HBD), one ring aromatic (RA), and two hydrophobic (HY) character. The developed hypothesis was then validated by different methods such as cost analysis, test set method, and Fischer’s test method for consistency. Hence, this validated model was further employed for screening of natural hit compounds from InterBioScreen Natural product database, consisting of more than 60,000 natural compounds and derivatives. The screened hit compounds were subsequently filtered by Lipinski’s rule of 5, ADME and toxicity parameters and molecular docking studies to remove the false positive rates. Finally, molecular docking analysis led to identification of the (3a′S,6a′R)-3′-(3,4-dihydroxybenzyl)-5′-(3,4-dimethoxyphenethyl)-5-ethyl-3′,3a′-dihydro-2′H-spiro[indoline-3,1′-pyrrolo[3,4-c]pyrrole]-2,4′,6′(5′H,6a′H)-trione (Compound ID: STOCK1N-65837) as potent hTLR7 modulator due to its better docking score and molecular interactions compared to other compounds. The result of virtual screening was further validated using molecular dynamics (MD) simulation analysis. Thus, a 30?ns MD simulation analysis revealed high stability and effective binding of STOCK1N-65837 within the binding site of hTLR7. Therefore, the present study provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 modulators with desired biological activity.     

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     

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

In silico studies on potential MCF-7 inhibitors: a combination of pharmacophore and 3D-QSAR modeling,virtual screening,molecular docking,and pharmacokinetic analysis

Gallic acid and its derivatives exhibit a diverse range of biological applications, including anti-cancer activity. In this work, a data-set of forty-six molecules containing the galloyl moiety, and known to show anticarcinogenic activity against the MCF-7 human cancer cell line, have been chosen for pharmacophore modeling and 3D-Quantitative Structure Activity Relationship (3D-QSAR) studies. A tree-based partitioning algorithm has been used to find common pharmacophore hypotheses. The QSAR model was generated for three, four, and five featured hypotheses with increasing PLS factors and analyzed. Results for five featured hypotheses with three acceptors and two aromatic rings were the best out of all the possible combinations. On analyzing the results, the most robust (R^2?=?.8990) hypothesis with a good predictive power (Q^2?=?.7049) was found to be AAARR.35. A good external validation (R² = .6109) was also obtained. In order to design new MCF-7 inhibitors, the QSAR model was further utilized in pharmacophore-based virtual screening of a large database. The predicted IC₅₀ values of the identified potential MCF-7 inhibitors were found to lie in the micromolar range. Molecular docking into the colchicine domain of tubulin was performed in order to examine one of the probable mechanisms. This revealed various interactions between the ligand and the active site protein residues. The present study is expected to provide an effective guide for methodical development of potent MCF-7 inhibitors.     

BCR-ABL tyrosine kinase inhibitor pharmacophore model derived from a series of phenylaminopyrimidine-based (PAP) derivatives

To reveal novel insights into the inhibition of BCR-ABL tyrosine kinase, pharmacophore mapping studies were performed for a series of phenylaminopyrimidine-based (PAP) derivatives, including imatinib (Gleevec). A seven-point pharmacophore model with one hydrophobic group (H), two hydrogen bond donors (D) and four aromatic rings (R) was developed using phase (pharmacophore alignment & scoring engine). The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of 0.886 and a survival score of 4.97 for training set molecules. The model showed excellent predictive power, with a correlation coefficient of Q² = 0.768 for an external test set of ten molecules. The results obtained from our studies provide a valuable tool for designing new lead molecules with potent activity.     

Identification of InhA inhibitors: A combination of virtual screening,molecular dynamics simulations and quantum chemical studies

In the present work, multiple pharmacophore-based virtual screening of the SPECS natural product database was carried out to identify novel inhibitors of the validated biological target, InhA. The pharmacophore models were built from the five different groups of the co-crystallized ligands present within the active site. The generated models with the same features from each group were pooled and subjected to the test set validation, receiver–operator characteristic analysis and Güner–Henry studies. A set of five hypotheses with sensitivity > 0.5, specificity > 0.5, area under curve (AUC) > 0.7, and goodness of hit score > 0.7 were retrieved and exploited for the virtual screening. The common hits (87 molecules) obtained from these hypotheses were processed via drug-likeness filters. The filtered molecules (27 molecules) were compared for the binding modes and the top scored molecules (12 molecules) along with the reference (triclosan (TCL), docking score = ?11.65 kcal/mol) were rescored and reprioritized via molecular mechanics-generalized Born surface area approach. Eventually, the stability of reprioritized (10 molecules) docked complexes was scrutinized via molecular dynamics simulations. Moreover, the quantum chemical studies of the dynamically stable compounds (9 molecules) were performed to understand structural features essential for the activity. Overall, the protocol resulted in the recognition of nine lead compounds that can be targeted against InhA.     

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.     

A combination of pharmacophore modeling,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC₅₀ ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R^2?=?.9949), cross validation coefficient (Q^2?=?.7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R² value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU–ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.