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     

3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation

Abstract

Mesenchymal-epithelial transition factor (c-Met) is a member of receptor tyrosine kinase. It involves in various cellular signaling pathways which includes proliferation, motility, migration, and invasion. Over-expression of c-Met has been reported in various cancers. Hence, it is an ideal therapeutic target for cancer. The main objective of the study is to identify crucial residues involved in the inhibition of c-Met kinase and to design a series of potent imidazo [4,5-b] pyrazine derivatives as c-Met inhibitors. Docking was used to identify important active site residues involved in the inhibition of c-Met kinase which was further validated by 100 ns of molecular dynamics simulation and free energy calculation using molecular mechanics generalized born surface area. Furthermore, binding energy decomposition identified that residues Tyr1230, Met1211, Asp1222, Tyr1159, Met1160, Val1092, Ala1108, and Leu1157 contributed favorably to the binding stability of compound 32. Receptor-guided Comparative Molecular Field Analysis (CoMFA) (q² = 0.751, NOC = 6, r² = 0.933) and Comparative Molecular Similarity Indices Analysis (COMSIA) (q² = 0.744, NOC = 6, r² = 0.950) models were generated based on the docked conformation of the most active compound 32. The robustness of these models was tested using various validation techniques and found to be predictive. The results of CoMFA and CoMSIA contour maps exposed the regions favorable to enhance the activity. Based on this information, 27 novel c-Met inhibitors were designed. These designed compounds exhibited potent activity than the most active compound of the existing dataset.

Communicated by Ramaswamy H. Sarma     

3D-QSAR and molecular modeling studies on 2,3-dideoxy hexenopyranosid-4-uloses as anti-tubercular agents targeting alpha-mannosidase

Ligand-based and structure-based methods were applied in combination to exploit the physicochemical properties of 2,3-dideoxy hex-2-enopyranosid-4-uloses against Mycobacterium tuberculosis H37Rv. Statistically valid 3D-QSAR models with good correlation and predictive power were obtained with CoMFA steric and electrostatic fields (r² = 0.797, q² = 0.589) and CoMSIA with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r² = 0.867, q² = 0.570) based on training set of 33 molecules with predictive r² of 0.808 and 0.890 for CoMFA and CoMSIA respectively. The results illustrate the requirement of optimal alkyl chain length at C-1 position and acceptor groups along hydroxy methyl substituent of C-6 to enhance the anti-tubercular activity of the 2,3-dideoxy hex-2-enopyranosid-4-uloses while any substitution at C-3 position exert diminishing effect on anti-tubercular activity of these enulosides. Further, homology modeling of M. tuberculosis alpha-mannosidase followed by molecular docking and molecular dynamics simulations on co-complexed models were performed to gain insight into the rationale for binding affinity of selected inhibitors with the target of interest. The comprehensive information obtained from this study will help to better understand the structural basis of biological activity of this class of molecules and guide further design of more potent analogues as anti-tubercular agents.     

In silico study of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanones derivatives as CCR1 antagonist: Homology modeling,docking and 3D-QSAR approach

C–C chemokine receptor type 1 (CCR1) is a chemokine receptor with seven transmembrane helices and it belongs to the G-Protein Coupled receptor (GPCR) family. It plays an important role in rheumatoid arthritis, organ transplant rejection, Alzheimer’s disease and also causes inflammation. Because of its role in disease processes, CCR1 is considered to be an important drug target. In the present study, we have performed three dimensional Quantitative Structure activity relationship (3D-QSAR) studies on a series of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives targeting CCR1. Homology modeling of CCR1 was performed based on a template structure (4EA3) which has a high sequence identity and resolution. The highest active molecule was docked into this model. Ligand-based and Receptor-based quantitative structure–activity relationship (QSAR) study was performed and CoMFA models with reasonable statistics was developed for both ligand-based (q² = 0.606; r² = 0.968) and receptor-guided (q² = 0.640; r² = 0.932) alignment methods. Contour map analyses identified favorable regions for high affinity binding. The docking results highlighted the important active site residues. Tyr113 was found to interact with the ligand through hydrogen bonding. This residue has been considered responsible for anchoring ligands inside the active site. Our results could also be helpful to understand the inhibitory mechanism of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives thereby to design more effective ligands in the future.     

A highly predictive 3D-QSAR model for binding to the voltage-gated sodium channel: Design of potent new ligands

A comprehensive comparative molecular field analysis (CoMFA) model for the binding of ligands to the neuronal voltage-gated sodium channel was generated based on 67 diverse compounds. Earlier published CoMFA models for this target provided μM ligands, but the improved model described here provided structurally novel compounds with low nM IC₅₀. For example, new compounds 94 and 95 had IC₅₀ values of 129 and 119 nM, respectively.     

The substrate specificity of SARS coronavirus 3C-like proteinase

The 3C-like proteinase of severe acute respiratory syndrome coronavirus (SARS) has been proposed to be a key target for structural based drug design against SARS. We have designed and synthesized 34 peptide substrates and determined their hydrolysis activities. The conserved core sequence of the native cleavage site is optimized for high hydrolysis activity. Residues at position P4, P3, and P3' are critical for substrate recognition and binding, and increment of beta-sheet conformation tendency is also helpful. A comparative molecular field analysis (CoMFA) model was constructed. Based on the mutation data and CoMFA model, a multiply mutated octapeptide S24 was designed for higher activity. The experimentally determined hydrolysis activity of S24 is the highest in all designed substrates and is close to that predicted by CoMFA. These results offer helpful information for the research on the mechanism of substrate recognition of coronavirus 3C-like proteinase.     

Molecular docking and 3D-QSAR on 2-(oxalylamino) benzoic acid and its analogues as protein tyrosine phosphatase 1B inhibitors

In this paper, molecular docking technique was used to investigate the binding conformation of twelve 2-(oxalylamino) benzoic acid (OBA) inhibitors in the active site of PTP1B. The predicted binding affinities are linearly correlated to the experimental values (r² = 0.859). Furthermore, comparative molecular field analysis (CoMFA) was conducted based on the binding conformation predicted by molecular docking. The predicted CoMFA model has satisfactory statistical significance and good actual predicted power. The information from molecular docking and CoMFA may give us some valuable hints to the optimization of lead compounds.     

Quantitative structure-activity relationship by CoMFA for cyclic urea and nonpeptide-cyclic cyanoguanidine derivatives on wild type and mutant HIV-1 protease

Abstact 3D-QSAR studies using the Comparative Molecular Field Analysis (CoMFA) methodology were conducted to predict the inhibition constants, K_i, and the inhibitor concentrations, IC₉₀ of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. A significant cross-validated correlation coefficient (q²) of 0.63 and a fitted correlation coefficient r² of 0.70 were obtained, indicating that the models can predict the anti-protease activity from poorly to highly active compounds reliably. The best predictions were obtained for: XV643 (predicted log 1/K_i=9.86), a 3,5-dimethoxy-benzyl cyclic urea derivate (molec60, predicted log 1/K_i=8.57) and a benzyl cyclic urea derivate (molec 61, predicted log 1/IC₉₀=6.87). Using the CoMFA method, we also predicted the biological activity of 14 cyclic urea derivatives that inhibit the HIV-1 protease mutants V82A, V82I and V82F. The predicted biological activities of the: (i) XNO63 (inhibitory activity on the mutant HIV-1 PR V82A), (ii) SB570 (inhibiting the mutant HIV-1 PR V82I) and also (iii) XV652 (during the interaction with the mutant HIV-1 PR V82F) were in good agreement with the experimental values.Figure Stereoview of the contour plots of the CoMFA steric and electrostatic fields. The favorable (indicated by blue polyhedra) and unfavorable (represented by red polyhedra) electrostatic areas and also the favorable (shown by green polyhedra) and unfavorable (shown by yellow polyhedra) steric areas formed around the most active molecule, 6a.     

Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations

Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r²_ncv and r²_loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r²_Pred) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.