Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR,molecular docking and molecular dynamic simulations

Abstract

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q² and R² values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor–ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.

Communicated by Ramaswamy H. Sarma     

Discovery of FIXa inhibitors by combination of pharmacophore modeling,molecular docking,and 3D-QSAR modeling

Human Coagulation Factor IXa (FIXa), specifically inhibited at the initiation stage of the blood coagulation cascade, is an excellent target for developing selective and safe anticoagulants. To explore this inhibitory mechanism, 86 FIXa inhibitors were selected to generate pharmacophore models and subsequently SAR models. Both best pharmacophore model and ROC curve were built through the Receptor–Ligand Pharmacophore Generation module. CoMFA model based on molecular docking and PLS factor analysis methods were developed. Model propagations values are q²?=?0.709, r²?=?0.949, and r²_pred?=?0.905. The satisfactory q² value of 0.609, r² value of 0.962, and r²_pred value of 0.819 for CoMSIA indicated that the CoMFA and CoMSIA models are both available to predict the inhibitory activity on FIXa. On the basis of pharmacophore modeling, molecular docking, and 3D-QSAR modeling screening, six molecules are screened as potential FIXa inhibitors.     

Structural determinants of benzodiazepinedione/peptide-based p53-HDM2 inhibitors using 3D-QSAR, docking and molecular dynamics

As a tumor suppressor, p53 protein regulates the cell cycle and is involved in preventing tumorgenesis. The protein level of p53 is under the tight control of its negative regulator human double minute 2 (HDM₂) via ubiquitination. Therefore, the design of inhibitors of HDM₂ has attracted much interest of research on developing novel anticancer drugs. Presently, two classes of molecules, i.e., the 1,4-benzodiazepine-2,5-diones (BDPs) and N-Acylpolyamine (NAPA) derivatives were studied by three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling approaches including the comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) as promising p53-HDM₂ inhibitors. Based on both the ligand-based and receptor-guided (docking) alignments, two optimal 3D-QSAR models were obtained with good predictive power of q ² = 0.41, r ² _pred = 0.60 for BDPs, and q ² = 0.414, r ² _pred = 0.69 for NAPA analogs, respectively. By analysis of the model and its related contour maps, it is revealed that the electrostatic interactions contributed much larger to the compound binding affinity than the steric effects. And the contour maps intuitively suggested where to modify the molecular structures in order to improve the binding affinity. In addition, molecular dynamics simulation (MD) study was also carried out on the dataset with purpose of exploring the detailed binding modes of ligand in the HDM₂ binding pocket. Based on the CoMFA contour maps and MD-based docking analyses, some key structural aspects responsible for inhibitory activity of these two classes of compounds were concluded as follows: For BDPs, the R₁ and R₃ regions should have small electronegativity groups; substituents R₂ and R₄ should be larger, and R₃ substituent mainly involves in H-bonds forming. For NAPA derivatives, bulky and electropositive groups in ring B and ring A, small substituent at region P is favorable for the inhibitory activity. The models and related information, we hope, may provide important insight into the inhibitor-p53-HDM₂ interactions and be helpful for facilitating the design of novel potent inhibitors.     

3D-QSAR,molecular docking and molecular dynamics studies of a series of RORγt inhibitors

The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R²_cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R²_pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.     

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.     

Binding conformations and QSAR of CA-4 analogs as tubulin inhibitors

A theoretical study on the binding conformations and the quantitative structure–activity relationship (QSAR) of combretastatin A4 (CA-4) analogs as inhibitors toward tubulin has been carried out using docking analysis and comparative molecular field analysis (CoMFA). The appropriate binding orientations and conformations of these compounds interacting with tubulin were revealed by the docking study; and a 3D-QSAR model showing significant statistical quality and satisfactory predictive ability was established, in which the correlation coefficient (R²) and cross-validation coefficient (q²) were 0.955 and 0.66, respectively. The same model was further applied to predict the pIC₅₀ values for 16 congeneric compounds as external test set, and the predictive correlation coefficient R²_pred reached 0.883. Other tests on additional validations further confirmed the satisfactory predictive power of the model. In this work, it was very interesting to find that the 3D topology structure of the active site of tubulin from the docking analysis was in good agreement with the 3D-QSAR model from CoMFA for this series of compounds. Some key structural factors of the compounds responsible for cytotoxicity were reasonably presented. These theoretical results can offer useful references for understanding the action mechanism and directing the molecular design of this kind of inhibitor with improved activity.     

Probing the structural requirements of A-type Aurora kinase inhibitors using 3D-QSAR and molecular docking analysis

Aurora-A, the most widely studied isoform of Aurora kinase overexpressed aberrantly in a wide variety of tumors, has been implicated in early mitotic entry, degradation of natural tumor suppressor p53 and centrosome maturation and separation; hence, potent inhibitors of Aurora-A may be therapeutically useful drugs in the treatment of various forms of cancer. Here, we report an in silico study on a group of 220 reported Aurora-A inhibitors with six different substructures. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques on this series of molecules. The resultant optimum 3D-QSAR models exhibited an r _cv² value of 0.404-0.582 and their predictive ability was validated using an independent test set, ending in r _pred² 0.512-0.985. In addition, docking studies were employed to explore these protein–inhibitor interactions at the molecular level. The results of 3D-QSAR and docking analyses validated each other, and the key structural requirements affecting Aurora-A inhibitory activities, and the influential amino acids involved were identified. To the best of our knowledge, this is the first report on 3D-QSAR modeling of Aurora-A inhibitors, and the results can be used to accurately predict the binding affinity of related analogues and also facilitate the rational design of novel inhibitors with more potent biological activities.     

Deciphering the crucial molecular properties of a series of Benzothiazole Hydrazone inhibitors that targets anti-apoptotic Bcl-xL protein

The Bcl-2 family proteins are the central regulators of apoptosis. Due to its predominant role in cancer progression, the Bcl-2 family proteins act as attractive therapeutic targets. Recently, molecular series of Benzothiazole Hydrazone (BH) inhibitors that exhibits drug-likeness characteristics, which selectively targets Bcl-xL have been reported. In the present study, docking was used to explore the plausible binding mode of the highly active BH inhibitor with Bcl-xL; and Molecular Dynamics (MD) simulation was applied to investigate the stability of predicted conformation over time. Furthermore, the molecular properties of the series of BH inhibitors were extensively investigated by pharmacophore based 3D-QSAR model. The docking correctly predicted the binding mode of the inhibitor inside the Bcl-xL hydrophobic groove, whereas the MD-based free energy calculation exhibited the binding strength of the complex over the time period. Furthermore, the residue decomposition analysis revealed the major energy contributing residues – F105, L108, L130, N136, and R139 – involved in complex stability. Additionally, a six-featured pharmacophore model – AAADHR.89 – was developed using the series of BH inhibitors that exhibited high survival score. The statistically significant 3D-QSAR model exhibited high correlation co-efficient (R² = .9666) and cross validation co-efficient (Q² = .9015) values obtained from PLS regression analysis. The results obtained from the current investigation might provide valuable insights for rational drug design of Bcl-xL inhibitor synthesis.     

Studies of H4R antagonists using 3D-QSAR,molecular docking and molecular dynamics

Three-dimensional quantitative structure–activity relationship studies were performed on a series of 88 histamine receptor 4 (H4R) antagonists in an attempt to elucidate the 3D structural features required for activity. Several in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), molecular docking, and molecular dynamics (MD), were carried out. The results show that both the ligand-based CoMFA model (Q ² = 0.548, R _ncv² = 0.870, R _pre² = 0.879, SEE = 0.410, SEP = 0.386) and the CoMSIA model (Q ² = 0.526, R _ncv² =0.866, R _pre² = 0.848, SEE = 0.416, SEP = 0.413) are acceptable, as they show good predictive capabilities. Furthermore, a combined analysis incorporating CoMFA, CoMSIA contour maps and MD results shows that (1) compounds with bulky or hydrophobic substituents at positions 4–6 in ring A (R2 substituent), positively charged or hydrogen-bonding (HB) donor groups in the R1 substituent, and hydrophilic or HB acceptor groups in ring C show enhanced biological activities, and (2) the key amino acids in the binding pocket are TRP67, LEU71, ASP94, TYR95, PHE263 and GLN266. To our best knowledge, this work is the first to report the 3D-QSAR modeling of these H4R antagonists. The conclusions of this work may lead to a better understanding of the mechanism of antagonism and aid in the design of new, more potent H4R antagonists.     

Pharmacophore mapping,molecular docking and QSAR studies of structurally diverse compounds as CYP2B6 inhibitors

Pharmacophore mapping, molecular docking and quantitative structure–activity relationship (QSAR) studies were carried out for a structurally diverse set of 48 compounds as CYP2B6 inhibitors. The generated best pharmacophore hypotheses from the three methods of conformer generation (FAST, BEST and conformer algorithm based on energy screening and recursive buildup) indicate the importance of two features, namely, hydrogen bond acceptor [electron-rich centre] and ring aromaticity. The distance between the two centres of the important features for ideal inhibitors varied from 5.82 to 6.03 Å. The chemometric tools used for the QSAR analysis were genetic function approximation (GFA) and genetic partial least squares. The developed QSAR models indicate the importance of an electron-rich centre, size of molecule, impact of branching and ring system and distribution of charges in the molecular surface. The docking study confirms the importance of an electron-rich centre for binding with the iron atom of the cytochrome enzyme. A GFA model with spline option was found to be the best model based on internal validation as well as the r ² _{m (overall)} criterion (Q ² = 0.772, r ² _{m (overall)} = 0.774). According to the external prediction statistics (R ² _pred = 0.876), another GFA-derived model with spline option outperforms the remaining models.     

Exploration of potential RSK2 inhibitors by pharmacophore modelling,structure-based 3D-QSAR,molecular docking study and molecular dynamics simulation

Abstract

The p90 ribosomal s6 kinase 2 (RSK2) is a promising target because of its over expression and activation in human cancer cells and tissues. Over the last few years, significant efforts have been made in order to develop RSK2 inhibitors to treat myeloma, prostatic cancer, skin cancer and etc., but with limited success so far. In this paper, pharmacophore modelling, molecular docking study and molecular dynamics (MD) simulation have been performed to explore the novel inhibitors of RSK2. Pharmacophore models were developed by 95 molecules having pIC₅₀ ranging from 4.577 to 9.000. The pharmacophore model includes one hydrogen bond acceptor (A), one hydrogen bond donor (D), one hydrophobic feature (H) and one aromatic ring (R). It is the best pharmacophore hypothesis that has the highest correlation coefficient (R² = 0.91) and cross validation coefficient (Q² = 0.71) at 5 component PLS factor. It was evaluated using enrichment analysis and the best model was used for virtual screening. The constraints used in this study were docking score, ADME properties, binding free energy estimates and IFD Score to screen the database. Ultimately, 12 hits were identified as potent and novel RSK2 inhibitors. A 15 ns molecular dynamics (MD) simulation was further employed to validate the reliability of the docking results.     

Exploring the structural determinants of novel xanthine derivatives as A2B adenosine receptor antagonists: a computational study

Adenosine is a ubiquitous endogenous nucleoside that controls numerous physiological functions via interacting with its specific G-coupled receptors. Activation of adenosine receptors (AdoRs), particularly A_2B AdoRs promotes the release of inflammatory cytokines; reduces vascular permeabilization and induces angiogenesis, thereby making A_2B AdoR becomes a potentially pharmacological target for drug development. Presently, for investigating the structural determinants of 164 xanthine derivatives as A_2B AdoR antagonists, we performed an in silico study integrating with 3D-QSAR, docking and molecular dynamics (MD) simulation. The obtained optimal model shows strong predictability (Q²?=?0.647, R²_ncv?=?0.955, and R²_pred?=?0.848). Additionally, to explore the binding mode of the ligand with A_2B AdoR and to understand their binding mechanism, docking analysis, MD simulations (20?ns), and the calculation of binding free energy were also carried out. Finally, the structural determinants of these xanthine derivatives were identified and a total of 20 novel A_2B AdoR antagonists with improved potency were computationally designed, and their synthetic feasibility and selectivity were also evaluated. The information derived from the present study offers a better appreciation for exploring the interaction mechanism of the ligand with A_2B AdoR, which could be helpful for designing novel potent A_2B AdoR antagonists.

Communicated by Ramaswamy H. Sarma     

Molecular modelling studies on d-annulated benzazepinones as VEGF-R2 kinase inhibitors using docking and 3D-QSAR

Chemotypes comprising the d-annulated 1,3-dihydro-2H-1-benzazepin-2-one scaffold derived from the paullone structure were found to be potent vascular endothelial growth factor receptor 2 (VEGF-R2) kinase inhibitors. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies were performed on a series of d-annulated benzazepinones with VEGF-R2 kinase inhibition activities. The comparative molecular field analysis and comparative molecular similarity indices analysis models using 32 molecules in the training set gave r²_cv values of 0.811 and 0.769, r² values of 0.962 and 0.953, respectively. 3D contour maps generated from the two models revealed that the electron-withdrawing groups at R₁ and the bulky, electron-withdrawing as well as hydrogen bond donor groups at R₂ position are favourable; the bulky, hydrogen bond acceptor substituent at R₃ and the minor groups at R₄ position may benefit the potency. We have designed a series of novel VEGF-R2 inhibitors by utilizing the SAR results revealed in the present study, which were predicted with excellent potencies in the developed models. The results may aid in designing of potential VEGF-R2 inhibitors with better activities.     

Pharmacophore generation,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on benzamide analogues as FtsZ inhibitors

FtsZ is an appealing target for the design of antimicrobial agent that can be used to defeat the multidrug-resistant bacterial pathogens. Pharmacophore modelling, molecular docking and molecular dynamics (MD) simulation studies were performed on a series of three-substituted benzamide derivatives. In the present study a five-featured pharmacophore model with one hydrogen bond acceptors, one hydrogen bond donors, one hydrophobic and two aromatic rings was developed using 97 molecules having MIC values ranging from .07 to 957 μM. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R² = .8319), cross validated coefficient (Q² = .6213) and a high Fisher ratio (F = 103.9) with three component PLS factor. A good correlation between experimental and predicted activity of the training (R² = .83) and test set (R² = .67) molecules were displayed by ADHRR.1682 model. The generated model was further validated by enrichment studies using the decoy test and MAE-based criteria to measure the efficiency of the model. The docking studies of all selected inhibitors in the active site of FtsZ protein showed crucial hydrogen bond interactions with Val 207, Asn 263, Leu 209, Gly 205 and Asn-299 residues. The binding free energies of these inhibitors were calculated by the molecular mechanics/generalized born surface area VSGB 2.0 method. Finally, a 15 ns MD simulation was done to confirm the stability of the 4DXD–ligand complex. On a wider scope, the prospect of present work provides insight in designing molecules with better selective FtsZ inhibitory potential.     

Molecular docking and CoMFA studies of thiazoloquin(az)olin(on)es as CD38 inhibitors: determination of inhibitory mechanism,pharmacophore interactions,and design of new inhibitors

In this research, molecular docking and 3D-QSAR studies were carried out on a series of 79 thiazoloquin(az)olin(on)es as CD38 inhibitors. Based on docking results, four interactions including hydrogen bonding with main chain of GLU-226 (H-M-GLU-226), Van der Waals interactions with side chain of TRP-125 (V-S-TRP-125), TRP-189 (V-S-TRP-189), and THR-221 (V-S-THR-221) were considered as pharmacological interactions. Active conformation of each ligand was extracted from docking studies and was used for carrying out 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) was performed on CD38 inhibitory activities of these compounds on human and mouse. We developed CoMFA models with five components as optimum models for both data-sets. For human data-set, a model with high predictive power was developed. R², RMSE, and F-test values for training set of this model were .94, .24, and 179.58, respectively, and R² and RMSE for its test set were .92 and .32, respectively. The q² and RMSE values for leave-one-out cross validation test on training set were .78 and .46, respectively, that demonstrate created model is robust. Based on extracted steric and electrostatic contour maps for this model, three inhibitors with pIC₅₀ larger than 8.85 were designed.     

2D-QSAR study,molecular docking,and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5)

Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R², and R²_ADJ, proved to be credible. The cross-validations for the models (q²_LOO = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r²_test = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.