QSAR studies and pharmacophore identification for arylsubstituted cycloalkenecarboxylic acid methyl esters with affinity for the human dopamine transporter

Data from a series of 29 monoamine transport inhibitors were used to generate 2D and 3D QSAR models for their binding affinity to the human dopamine transporter (hDAT). Among the inhibitors were many non-nitrogen containing compounds. The 2D QSAR analysis resulted in the equation -logK(i)=4.00-3.93E(LUMO)-0.67E(HOMO)-3.24sigma(p), which predicted the importance of electron withdrawing groups in the aromatic moiety. However, the model failed to predict the observed poor binding of nitro-substituted compounds. In contrast, a derived 3D QSAR model was capable of predicting these more correctly.     

Computational modeling and design of renin inhibitors

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r² (pred) of ～0.67 and rmse of 0.79 was achieved for an external validation set of ～150 compounds centered on the model developed using ～450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ～2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.     

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.     

Combined 3D QSAR and molecular docking studies to reveal novel cannabinoid ligands with optimum binding activity

The combination of NMR spectroscopy and molecular modeling studies provided the putative bioactive conformation for the analgesic cannabinoid (CB) ligand (−)-2-(6a,7,10,10a-tetrahydro-6,6,9-trimethylhydroxy-6H-dibenzo[b,d]pyranyl)-2-hexyl 1,3-dithiolane which served as a template in reported three-dimensional quantitative structure–activity relationship (3D QSAR) studies [Durdagi et al., J. Med. Chem. 2007, 50, 2875]. The reported 3D models of the CB1 receptor allowed us to construct a new 3D QSAR model based on theoretical calculations and molecular docking studies. Statistical comparison of the constructed two 3D QSAR studies showed the improvement of the new model. In addition, the new model can explain more effectively the experimental data and thus it can serve more efficiently in the rational drug design of pharmacologically optimized CB analogues.     

Docking and 3-D QSAR studies of dual PDE4-PDE7 inhibitors†

Small dual-specificity molecules inhibiting PDE4 and PDE7 can be used to treat inflammatory diseases. To design and synthesize dual PDE4 and PDE7 inhibitors, we carried out the target-based docking and the 3D QSAR study using CoMFA. Three compounds were synthesized. We predicted their inhibitory activities using our 3D QSAR model and tested their activities against PDE4 and PDE7 in vitro.     

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.     

3D QSAR studies on GSK-3 inhibition by aloisines

GSK-3 is involved in various physiological processes and its inhibitors have been evaluated as promising drug candidates for a lot of unmet pathologies. In this paper, inhibition of GSK-3 by aloisines is investigated by 3D QSAR studies. Two alignment rules were applied to check the influence of spatial alignment of the compounds. Both the CoMFA and CoMSIA techniques were carried out and ASS procedure was applied for CoMFA to find a satisfactory model. The best QSAR model obtained is a CoMSIA model characterized with r(2) of 0.938 and q(2) of 0.673 including steric, electrostatic and hydrophobic fields, possessing good predicting ability. To get a better understanding of the relationship between chemical structure and biological activity, a complex structure of aloisine with GSK-3 was obtained by superimposing GSK-3 into the known cocrystal structure of aloisine-CDK2, and then factors that affect the inhibition activity were investigated further, combining the QSAR study with the complex structure, the results of which are in good accordance and complementary to each other.     

In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a negative regulator of insulin and leptin signalling pathway; hence, it can be considered as a new therapeutic target of intervention for the treatment of type 2 diabetes. Inhibition of this molecular target takes care of both diabetes and obesity, i.e. diabestiy. In order to get more information on identification and optimization of lead, pharmacophore modelling, atom-based 3D QSAR, docking and molecular dynamics studies were carried out on a set of ligands containing thiazolidine scaffold. A six-point pharmacophore model consisting of three hydrogen bond acceptor (A), one negative ionic (N) and two aromatic rings (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The probable binding conformation of the ligands within the active site was studied through molecular docking. The molecular interactions and the structural features responsible for PTP1B inhibition and selectivity were further supplemented by molecular dynamics simulation study for a time scale of 30 ns. The present investigation has identified some of the indispensible structural features of thiazolidine analogues which can further be explored to optimize PTP1B inhibitors.     

3D QSAR of novel estrogen–RGD peptide conjugates: Getting insight into structural dependence of anti-osteoporosis activity and side effect of estrogen in ERT

To explore the structural dependence of the oral potency and side effect of estrogen–RGD peptide conjugates, here six novel conjugates were prepared via introducing RGD-tetrapeptides into both 3- and 17-positions of estradiol, and introducing RGD-octapeptides into 3-position of estrone. In an ovariectomized mouse model they exhibited higher anti-osteoporosis activity and lower side effect than estrogen. For 3D QSAR analysis the anti-osteoporosis activities of nine known conjugates estrogen–RGD tetrapeptide conjugates were also provided. Using Cerius² module their 3D QSAR analysis was performed, four equations with high r² values were established, and the structural dependence of the oral potency and side effect of them was elucidated.     

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation,protein–ligand interaction fingerprint, 3D pharmacophore,docking and 3D QSAR

Abstract

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.

Communicated by Ramaswamy H. Sarma     

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC₅₀ values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q²_LOO, Q²_(F2), and Q²_(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.     

Pharmacophore mapping and in silico screening to identify new potent leads for A(2A) 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.     

Homology modeling and QSAR analysis of 1,3,4-thiadiazole and 1,3,4-triazole derivatives as carbonic anhydrase inhibitors

Carbonic anhydrase (CA) inhibitors are very interesting target for designing anticancer (hypoxic) and antiglaucoma drugs. In the present study, a 3D homology modeling of human carbonic anhydrase-IX (hCA-IX) isozyme, based upon the crystal structure of murine CA-XIVA (PDB CODE 1RJ5) was performed, as no experimental 3D structures are available. A homology model of hCA-IX was developed and validated. To explore the responsible physicochemical properties of 1,3,4-thiadiazole and 1,3,4-triazole derivatives for carbonic anhydrase inhibition, a quantitative structure activity relationship (QSAR) study was performed having hCA-II and hCA-IX inhibitory activity respectively. In hCA-II and hCA-IX inhibitory activities, four significant models with good correlations (> or = 0.945 & > or = 0.926) were obtained; two models (models 1 and 3) were selected based on statistical criterion. The QSAR study revealed that in case of hCA-II, overall increase in size and volume of molecule, introduction of electropositive surfaces might increase the inhibitory activity, whereas in case of hCA-IX, decreasing the hydrophobicity and introduction of electron releasing substituents might increase the hCA-IX inhibitory activity.