Molecular docking and 3D QSAR studies on 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes based on the structural modeling of human CCR5 receptor

In the present study, we have used an approach combining protein structure modeling, molecular dynamics (MD) simulation, automated docking, and 3D QSAR analyses to investigate the detailed interactions of CCR5 with their antagonists. Homology modeling and MD simulation were used to build the 3D model of CCR5 receptor based on the high-resolution X-ray structure of bovine rhodopsin. A series of 64 CCR5 antagonists, 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes, were docked into the putative binding site of the 3D model of CCR5 using the docking method, and the probable interaction model between CCR5 and the antagonists were obtained. The predicted binding affinities of the antagonists to CCR5 correlate well with the antagonist activities, and the interaction model could be used to explain many mutagenesis results. All these indicate that the 3D model of antagonist-CCR5 interaction is reliable. Based on the binding conformations and their alignment inside the binding pocket of CCR5, three-dimensional structure-activity relationship (3D QSAR) analyses were performed on these antagonists using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Both CoMFA and CoMSIA provide statistically valid models with good correlation and predictive power. The q(2)(r(cross)(2)) values are 0.568 and 0.587 for CoMFA and CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were not included in the training set. Mapping these models back to the topology of the active site of CCR5 leads to a better understanding of antagonist-CCR5 interaction. These results suggest that the 3D model of CCR5 can be used in structure-based drug design and the 3D QSAR models provide clear guidelines and accurate activity predictions for novel antagonist design.     

3D-QSAR pharmacophore modelling,virtual screening and docking studies for lead discovery of a novel scaffold for VEGFR 2 inhibitors: Design,synthesis and biological evaluation

A series of novel 6,7-dihydro-5H-cyclopenta[d]pyrimidine derivatives was successfully designed, synthesized and evaluated as a new chemical scaffold with vascular endothelial growth factor receptor (VEGFR 2) inhibitory activity. Compounds 6c and 6b showed enzyme inhibition of 97% and 87% at 10 µM, respectively, and exhibited potent dose-related VEGFR 2 inhibition with IC₅₀ values of 0.85 µM and 2.26 µM, respectively. The design of the 6,7-dihydro-5H-cyclopenta[d]pyrimidine scaffold was implemented via consecutive molecular modelling protocols prior to the synthesis and biological evaluation of the derivatives. First, sorafenib was docked in the binding site of VEGFR 2 to study its binding orientation and affinity, followed by the generation of a valid 3D QSAR pharmacophore model for use in the virtual screening of different 3D databases. Structures with promising pharmacophore-based virtual screening results were refined using molecular docking studies in the binding site of VEGFR 2. A novel scaffold was designed by incorporating the results of the pharmacophore model generation and molecular docking studies. The new scaffold showed hydrophobic interactions with the kinase front pocket that may be attributed to increasing residence time in VEGFR 2, which is a key success factor for ligand optimization in drug discovery. Different derivatives of the novel scaffold were validated using docking studies and pharmacophore mapping, where they exhibited promising results as VEGFR 2 inhibitors to be synthesized and biologically evaluated. 6,7-dihydro-5H-cyclopenta[d]pyrimidine is a new scaffold that can be further optimized for the synthesis of promising VEGFR 2 inhibitors.     

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained (q²_LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.     

In silico design of potent EGFR kinase inhibitors using combinatorial libraries

This paper is an attempt to design 4-anilinoquinazoline compounds having promising anticancer activities against epidermal growth factor (EGFR) kinase inhibition, using virtual combinatorial library approach. Partial least squares method has been applied for the development of a quantitative structure–activity relationship (QSAR) model based on training and test set approaches. The partial least squares model showed some interesting results in terms of internal and external predictability against EGFR kinase inhibition for such type of anilinoquinazoline derivatives. In virtual screening study, out of 4860 compounds in chemical library, 158 compounds were screened and finally, 10 compounds were selected as promising EGFR kinase inhibitors based on their predicted activities from the QSAR model. These derivatives were subjected to molecular docking study to investigate the mode of binding with the EGFR kinase, and the two compounds (ID 3639 and 3399) showing similar type of docking score and binding patterns with that of the existing drug molecules like erlotinib were finally reported.     

3D QSAR and docking study of flavone derivatives as potent inhibitors of influenza H1N1 virus neuraminidase

Although several flavonoids have been reported to exert inhibitory effects on influenza H1N1 neuraminidase (NA), little is known about the structure-activity relationship and binding mode. Three dimensional QSAR (quantitative structure-activity relationship) and molecular docking approaches were applied to explore the structural requisites of flavone derivatives for NA inhibitory activity. A meaningful QSAR model with R(2) of 0.5968, Q(2) of 0.6457, and Pearson-R value of 0.8679, was constructed. From the QSAR model, it could be seen how 6-OH, 3'-OH, 4'-OH, and 8-position substituent affect the NA inhibitory activity. Molecular docking study between the most active compound and NA suggested that hydrogen bonds, hydrophobic and electrostatic interactions were closely related to NA inhibitory activity, 5-OH and 7-OH may be essential for this activity. The results provide a set of useful guidelines for the rational design of novel NA inhibitors.     

In silico study of M18 aspartyl amino peptidase (M18AAP) of Plasmodium vivax as an antimalarial drug target

Plasmodium vivax (Pv) is the second most malaria causing pathogen among Plasmodium species. M18 aspartic aminopeptidase (M18AAP) protein is a single gene copy present in Plasmodium. This protein is functional at the terminal stage of hemoglobin degradation of host and completes the hydrolysis process which makes it an important target for new chemotherapeutics. No experimental and structural study on M18AAP protein of P. vivax is reported till today. This paper advocates the application of multiple computational approaches like protein model prediction, ligand-based 3D QSAR study, pharmacophore, structure-based virtual screening and molecular docking simulation for identification of potent lead molecules against the enzyme. The 3D QSAR model was developed using known bioactive compounds against the PvM18AAP protein which statistically signify the k-NN model with q^2 = 0.7654. The study reports a lead molecule from ligand-centric approach with good binding affinity and possessing lowest docking score. The findings will be helpful for in-vivo and in-vitro validations and development of potent anti-malarial molecules against the drug resistant strains of malaria parasite.     

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r² of 0.996; for the test set, the correlation coefficient r² was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.     

Theoretical studies on pyrimidine substituent derivatives as dual inhibitors of AP-1 and NF-κB

Theoretical studies on the three-dimensional (3D) quantitative structure-activity relationship (QSAR) and mechanisms of action of a series of pyrimidine substituent derivatives as dual inhibitors of AP-1 and NF-κB were carried out using comparative molecular field analysis (CoMFA) and docking methods. The established 3D-QSAR model exhibits a satisfying statistical quality and prediction ability. Docking results show somewhat lower average values of the flexible and rigid energy scores in the chosen binding sites. The docking analysis offers appropriate orientations and conformations of these compounds at the binding sites to both AP-1 and NF-κB in good agreement with the 3D-QSAR model from CoMFA. The combined CoMFA and docking study suggests the following substituent selections: substituent R₂ should be a kind of H–N–thienyl or CH₃–N–thienyl group; substituent R₅ should be a kind of COO–tBu or COOEt group; and substituent R₄ should be a CH₂CH₃ or 2-thienyl group. The docking analysis also shows that the binding sites fall just at the joint regions between AP-1 (or NF-κB) and DNA, where these compounds can effectively prevent free AP-1 and NF-κB from binding to DNA, and this may be the reason that derivatives with pyrimidine substituents have an inhibition function. In addition, a very interesting finding was that the binding sites of both AP-1 and NF-κB have a common structural characteristic, thereby providing a reasonable explanation for the dual inhibition functions of these compounds towards both AP-1 and NF-κB. These theoretical results help to deepen our understanding of the inhibition mechanism of these pyrimidine substituent derivatives, and will aid in directing further drug-molecular design.     

Synthesis,binding and bioactivity of γ-methylene γ-lactam ecdysone receptor ligands: Advantages of QSAR models for flexible receptors

Nuclear hormone receptors, such as the ecdysone receptor, often display a large amount of induced fit to ligands. The size and shape of the binding pocket in the EcR subunit changes markedly on ligand binding, making modelling methods such as docking extremely challenging. It is, however, possible to generate excellent 3D QSAR models for a given type of ligand, suggesting that the receptor adopts a relatively restricted number of binding site configurations or ‘attractors’. We describe the synthesis, in vitro binding and selected in vivo toxicity data for γ-methylene γ-lactams, a new class of high-affinity ligands for ecdysone receptors from Bovicola ovis (Phthiraptera) and Lucilia cuprina (Diptera). The results of a 3D QSAR study of the binding of methylene lactams to recombinant ecdysone receptor protein suggest that this class of ligands is indeed recognised by a single conformation of the EcR binding pocket.     

Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

Computational design of novel fullerene analogues as potential HIV-1 PR inhibitors: Analysis of the binding interactions between fullerene inhibitors and HIV-1 PR residues using 3D QSAR, molecular docking and molecular dynamics simulations

A series of experimentally reported as well as computationally designed monoadducts and bisadducts of [60]fullerene analogues have been used in order to analyze the binding interactions between fullerene based inhibitors and HIV-1 PR employing docking studies. MD simulations of ligand-free and the inhibitor bound HIV-1 PR systems complemented the above studies and provided proper input structure of HIV-1 PR in docking simulations. The obtained results revealed a different orientation of the beta-hairpin flaps at these two systems. In inhibitor bound system, the flaps of the enzyme are pulled in toward the bottom of the active site (the closed form) while, in ligand-free system flaps shifted away from the dual Asp25 catalytic site and this system adopts a semi-open form. The structural analysis of these systems at catalytic and flexible flap regions of the HIV-1 PR through the simulation, assisted in understanding the structural preferences of these regions, as well as, the adopted orientations of fullerene derivatives within the active site of the enzyme. Five different combinations of steroelectronic fields of 3D QSAR/CoMSIA models were obtained from the set of biologically evaluated and computationally designed fullerene derivatives (training set=43, test set=6) in order to predict novel compounds with improved inhibition effect. The best 3D QSAR/CoMSIA model yielded a cross validated r(2) value of 0.739 and a non-cross validated r(2) value of 0.993. The derived model indicated the importance of steric (42.6%), electrostatic (12.7%), H-bond donor (16.7%) and H-bond acceptor (28.0%) contributions. The derived contour plots together with de novo drug design were then used as pilot models for proposing the novel analogues with enhanced binding affinities. Such structures may trigger the interest of medicinal chemists for novel HIV-1 PR inhibitors possessing higher bioactivity.     

Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains

To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from −8.4 to −9.4 kcal/mol.     

Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators

Comparative molecular surface analysis (CoMSA) with robust IVE-PLS variable elimination if tested for the benchmark CBG steroid series provides highly predictive RI 3D QSAR models, but failed however to model the activity of sulforaphane (SP) activators of quinone reductase. The application of the SP poses obtained from multipose molecular docking to model the RD IVE-PLS CoMSA resulted in a predictive form. This model indicated lipophilic potential as the activity determinant. The individual molecular surface areas of the highest contribution to the SP activity was identified and visualized by CoMSA contour plots.     

A combined approach of docking and 3D QSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Three-dimensional quantitative structure-activity relationship (3D QSAR) studies such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and docking simulations were conducted on a series of potent benzoylaminobenzoic acids. Docking studies were employed to position the inhibitors into the FabH active site to determine the probable binding conformation. A reasonable correlation between the predicated binding free energy and the inhibitory activity was found. CoMFA and CoMSIA were performed based on the docking conformations, giving q(2) of 0.637 and 0.697 for CoMFA and CoMSIA models, respectively. The predictive ability of the models was validated using a set of compounds that were not included in the training set and progressive scrambling test. Mapping the 3D QSAR models to the active site of FabH related that some important amino acid residues are responsible for protein-inhibitor interaction. These results should be applicable to the prediction of the activities of new FabH inhibitors, as well as providing structural understanding.