Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

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.     

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.     

Understanding the structure-activity relationship of betulinic acid derivatives as anti-HIV-1 agents by using 3D-QSAR and docking

Novel anti-HIV-1 agents derived from betulinic acid have been greatly concerned. 3D-QSAR and molecular docking studies were applied to rationalize the structural requirements responsible for the anti-HIV activity of these compounds. The CoMFA and CoMSIA models resulted from 28 molecules gave r _cv² values of 0.599 and 0.630, r ² values of 0.994 and 0.958, respectively. To estimate the predictive ability of the 3D-QSAR model, an external validation was employed. Based on the contour maps generated from both CoMFA and CoMSIA, we have identified some key features in the betulinic acid derivatives that are responsible for the anti-HIV activity. Molecular docking was used to explore the binding mode between these derivatives and HIV gp120. We have therefore designed a series of novel betulinic acid derivatives by utilizing the SAR results revealed in the present study, which were predicted with excellent potencies in the developed models. The results provide a valuable method to design new betulinic acid derivatives as anti-HIV-1 agents.     

In silico studies on p21-activated kinase 4 inhibitors: comprehensive application of 3D-QSAR analysis,molecular docking,molecular dynamics simulations,and MM-GBSA calculation

Abstract

P21-activated kinase 4 (PAK4) is a serine/threonine protein kinase, which is associated with many cancer diseases, and thus being considered as a potential drug target. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations were performed to explore the structure-activity relationship of a series of pyrropyrazole PAK4 inhibitors. The statistical parameters of comparative molecular field analysis (CoMFA, Q ² = 0.837, R ² = 0.990, and R ² _pred = 0.967) and comparative molecular similarity indices analysis (CoMSIA, Q ² = 0.720, R ² = 0.972, and R ² _pred = 0.946) were obtained from 3D-QSAR model, which exhibited good predictive ability and significant statistical reliability. The binding mode of PAK4 with its inhibitors was obtained through molecular docking study, which indicated that the residues of GLU396, LEU398, LYS350, and ASP458 were important for activity. Molecular mechanics generalized born surface area (MM-GBSA) method was performed to calculate the binding free energy, which indicated that the coulomb, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. Furthermore, through 100?ns MD simulations, we obtained the key amino acid residues and the types of interactions they participated in. Based on the constructed 3D-QSAR model, some novel pyrropyrazole derivatives targeting PAK4 were designed with improved predicted activities. Pharmacokinetic and toxicity predictions of the designed PAK4 inhibitors were obtained by the pkCSM, indicating these compounds had better absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Above research provided a valuable insight for developing novel and effective pyrropyrazole compounds targeting PAK4.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

Design,synthesis and biological evaluation of novel oseltamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is one of the particular potential targets for novel antiviral therapy. In this work, a series of neuraminidase inhibitors with the cyclohexene scaffold were studied based upon the combination of 3D-QSAR, molecular docking, and molecular dynamics techniques. The results indicate that the built 3D-QSAR models yield reliable statistical information: the correlation coefficient (r²) and cross-validation coefficient (q²) of CoMFA (comparative molecular field analysis) are 0.992 and 0.819; the r² and q² of CoMSIA (comparative molecular similarity analysis) are 0.992 and 0.863, respectively. Molecular docking and MD simulations were conducted to confirm the detailed binding mode of enzyme-inhibitor system. The new NA inhibitors had been designed, synthesized, and their inhibitory activities against group-1 neuraminidase were determined. One agent displayed excellent neuraminidase inhibition, with IC₅₀ value of 39.6?μM against NA, while IC₅₀ value for oseltamivir is 61.1?μM. This compound may be further investigated for the treatment of infection by the new type influenza virus.     

3D-QSAR and molecular docking studies of azaindole derivatives as Aurora B kinase inhibitors

The Aurora kinases have been regarded as attractive targets for the development of new anticancer agents. Recently a series of azaindole derivatives with Aurora B inhibitory activities were reported. To explore the relationship between the structures of substituted azaindole derivatives and their inhibition of Aurora B, 3D-QSAR and molecular docking studies were performed on a dataset of 41 compounds. 3D-QSAR, including CoMFA and CoMSIA, were applied to identify the key structures impacting their inhibitory potencies. The CoMSIA model showed better results than CoMFA, with r ² _cv value of 0.575 and r ² value of 0.987. 3D contour maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity. Based on the structure-activity relationship revealed by the present study, we have designed a set of novel Aurora B inhibitors that showed excellent potencies in the developed models. Thus, our results allowed us to design new derivatives with desired activities.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Molecular dynamics simulations reveal structural insights into inhibitor binding modes and mechanism of casein kinase II inhibitors

Casein kinase-II, a member of protein kinase family, plays significant role in different cellular processes such as cell growth, differentiation, proliferation, gene expression, and embryogenesis. Being a potent suppressor of apoptosis, it serves as a significant link for its association with various types of malignancies such as colorectal and breast cancer. To overcome its pathological role in various cancerous diseases, CK-II procures great consideration as a therapeutic target. This study aimed at understanding the binding mechanism and structural properties of benzimidazole derivatives by utilizing various computational tools including docking simulation, three-dimensional quantitative structure activity relationships and molecular dynamic simulation. Structure-based 3D-QSAR techniques such as CoMFA and CoMSIA models, were established from the conformations gained by protein–ligand docking approach. The attained models have showed a good extrapolative power for internal as well as external validation. Moreover, MD simulation was carried out to explain the detailed binding mechanism and the comparison of inhibitor’s binding mode with diverse biological activities. A good correlation was observed among docking studies, MD results, and contour map analysis. Interestingly new molecules were designed using detail structural information from MD simulation, showed higher potency of inhibition (pIC₅₀ 7.6–7.7) compare to the most active compound of the series.     

Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Selected ligands were docked into the active site of human HDAC1. Based on the docking results, a novel binding mode of indole amide analogues in the human HDAC1 catalytic core is presented, and enzyme/inhibitor interactions are discussed. The indole amide group is located in the open pocket, and anchored to the protein through a pair of hydrogen bonds with Asp99 O-atom and amide NH group on ligand. Based on the binding mode, predictive 3D-QSAR models were established, which had conventional r2 and cross-validated coefficient values (r(cv)2) up to 0.982 and 0.601 for CoMFA and 0.954 and 0.598 for CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as HDAC inhibitors. The CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic, lipophilicity, and hydrogen-bonding potential surface maps, as well as the docking studies, provided good insights into inhibitor-HDAC interactions at the molecular level. Based on these results, novel molecules with improved activity can be designed.     

Design,synthesis and biological evaluation of novel zanamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is an important antiviral drug target. Zanamivir is one of the most potent NA inhibitors. In this paper, a series of zanamivir derivatives as potential NA inhibitors were studied by combination of molecular modeling techniques including 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. The results show that the best CoMFA (comparative molecular field analysis) model has q²?=?0.728 and r²?=?0.988, and the best CoMSIA (comparative molecular similarity indices analysis) model has q²?=?0.750 and r²?=?0.981, respectively. The built 3D-QSAR models show significant statistical quality and excellent predictive ability. Seven new NA inhibitors were designed and predicted. 20?ns of MD simulations were carried out and their binding free energies were calculated. Two designed compounds were selected to be synthesized and biologically evaluated by NA inhibition and virus inhibition assays. One compound (IC₅₀?=?0.670?µM, SI?>?149) exhibits excellent antiviral activity against A/WSN/33 H1N1, which is superior to the reference drug zanamivir (IC₅₀?=?0.873?µM, SI?>?115). The theoretical and experimental results may provide reference for development of new anti-influenza drugs.     

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.     

3D-QSAR study of Chk1 kinase inhibitors based on docking

Checkpoint kinase 1 (Chk1), a kind of a serine/threonine protein kinase, plays a significant role in DNA damage-induced checkpoints. Chk1 inhibitors have been demonstrated to abrogate the S and G2 checkpoints and disrupt the DNA repair process, which results in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain at the G1 phase via p53 to repair their DNA damages, and are less influenced by the abrogation of S and G2 checkpoint. Therefore, selective inhibitors of Chk1 may be of great therapeutic value in cancer treatment. In this paper, in order to understand the structure-activity relationship of macro-cyclic urea Chk1 inhibitors, a study combined molecular docking and 3D-QSAR modeling was carried out, which resulted in two substructure-based 3D-QSAR models, including the CoMFA model (r(2), 0.873; q(2), 0.572) and CoMSIA model (r(2), 0.897; q(2), 0.599). The detailed microscopic structures of Chk1 binding with inhibitors were performed by molecular docking. Two docking based 3D-QSAR models were developed (CoMFA with r(2), 0.887; q(2), 0.501; CoMSIA with r(2), 0.872; q(2), 0.520). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures would be helpful to better understand the structure-activity relationship. All the conclusions drawn from both the 3D-QSAR contour maps and molecular docking were in accordance with the experimental activity dates. The results suggested that the developed models and the obtained CHk1 inhibitor binding structures might be reliable to predict the activity of new inhibitors and reasonable for the future drug design.     

Structure-based 3D-QSAR studies on thiazoles as 5-HT<Subscript>3</Subscript> receptor antagonists

Structure-based 3D-QSAR studies were performed on 20 thiazoles against their binding affinities to the 5-HT₃ receptor with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The thiazoles were initially docked into the binding pocket of a human 5-HT_3A receptor homology model, constructed on the basis of the crystal structure of the snail acetylcholine binding protein (AChBP), using the GOLD program. The docked conformations were then extracted and used to build the 3D-QSAR models, with cross-validated values 0.785 and 0.744 for CoMFA and CoMSIA, respectively. An additional five molecules were used to validate the models further, giving satisfactory predictive values of 0.582 and 0.804 for CoMFA and CoMSIA, respectively. The results would be helpful for the discovery of new potent and selective 5-HT₃ receptor antagonists.      

Docking-based 3D-QSAR (CoMFA,CoMFA-RG,CoMSIA) study on hydroquinoline and thiazinan-4-one derivatives as selective COX-2 inhibitors

A series of 26 selective COX-2 inhibitors which reported previously by our laboratory was selected to generate three-dimensional quantitative structure activity relationship (3D-QSAR) model. Active conformation of each molecule was predicted by docking studies and used for molecular alignment. Activity of 20 molecules as a train set was predicted using three methods including comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RG) and comparative molecular similarity index analysis (CoMSIA). The best models of CoMFA-RG and CoMSIA revealed correlation coefficients r² of 0.955 and 0.947, the leave one out cross-validation coefficients q² of 0.573 and 0.574, respectively. In addition, CoMFA-RG and CoMSIA models were validated by a test set of six molecules with predicted coefficients r²_pred of 0.644 and 0.799, respectively. Contour maps of generated models provided fruitful information about structural aspect of molecules that affected their COX-2 inhibitory activity. Based on three models results, steric and electrostatic properties are the most important factors in controlling the activity of the molecules. Results of CoMFA-RG and CoMSIA models were utilized to design new molecules. Comparison of experimental and predicted pIC₅₀ values of designed molecules indicated that CoMFA-RG had the more predictive ability.

Communicated by Ramaswamy H. Sarma     

Molecular docking and 3D-QSAR studies of HIV-1 protease inhibitors

HIV-1 protease is an obligatory enzyme in the replication process of the HIV virus. The abundance of structural information on HIV-1PR has made the enzyme an attractive target for computer-aided drug design strategies. The daunting ability of the virus to rapidly generate resistant mutants suggests that there is an ongoing need for new HIV-1PR inhibitors with better efficacy profiles and reduced toxicity. In the present investigation, molecular modeling studies were performed on a series of 54 cyclic urea analogs with symmetric P2/P2′ substituents. The binding modes of these inhibitors were determined by docking. The docking results also provided a reliable conformational superimposition scheme for the 3D-QSAR studies. To gain insight into the steric, electrostatic, hydrophobic and hydrogen-bonding properties of these molecules and their influence on the inhibitory activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed. Two different alignment schemes viz. receptor-based and atom-fit alignment, were used in this study to build the QSAR models. The derived 3D-QSAR models were found to be robust with statistically significant r ² and r ² _pred values and have led to the identification of regions important for steric, hydrophobic and electronic interactions. The predictive ability of the models was assessed on a set of molecules that were not included in the training set. Superimposition of the 3D-contour maps generated from these models onto the active site of enzyme provided additional insight into the structural requirements of these inhibitors. The CoMFA and CoMSIA models were used to design some new inhibitors with improved binding affinity. Pharmacokinetic and toxicity predictions were also carried out for these molecules to gauge their ADME and safety profile. The computational results may open up new avenues for synthesis of potent HIV-1 protease inhibitors.     

Molecular modeling studies on phosphonic acid-containing thiazole derivatives: design for fructose-1,6-bisphosphatase inhibitors

Presently, an in silico modeling was carried out on a series of 63 phosphonic acid-containing thiazole derivatives as fructose-1,6-bisphosphatase (FBPase) inhibitors using CoMFA/CoMSIA and molecular docking methods. The CoMFA and CoMSIA models using 51 molecules in the training set gave r _cv² values of 0.675 and 0.619, r ² values of 0.985 and 0.979, respectively. The systemic external validation indicated that our CoMFA and CoMSIA models possessed high predictive powers with r ₀² values of 0.995 and 0.994, r _m(test)² values of 0.887 and 0.860, respectively. The 3D contour maps of the CoMFA and CoMSIA provided smooth and interpretable explanation of the structure-activity relationship for the inhibitors. Molecular docking studies revealed that a phosphonic group was essential for binding to the AMP binding site, and some key features were also identified. The analyses of the 3D contour plots and molecular docking results permitted interesting conclusions about the effects of different substituent groups at different positions of the common scaffold, which might guide the design of novel FBPase inhibitors with higher activity and bioavailability. A set of 60 new analogues were designed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The findings can be quite useful to aid the designing of new fructose-1,6-biphophatase inhibitors with improved biological response.