CoMFA and CoMSIA studies on a new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line

Abstract  A new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line is examined to determine the relationship between the structural properties and the biological activity of these compounds—the 3-D quantitative structure–activity relationship (3D-QSAR)—using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA and CoMSIA models were obtained using the atom-based alignment of 33 compounds, 22 training compounds and 11 tested compounds, and these give desirable statistics; those for the CoMFA standard model were: r _cv² = 0.691, r ² = 0.998, S _press = 0.178, s = 0.014 and F = 1080.765, while CoMSIA combined steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields: r _cv² = 0.600, r ² = 0.988, S _press = 0.206, s = 0.034 and F = 284.433. The 3D-QSAR models calculated satisfactory test set activities. The 3D-QSAR contour plots correlated strongly with the experimental data for the binding topology. For this reason, these results would be beneficial for predicting affinities with the compounds of interest, and they are advantageous for guiding the design and synthesis of new and more effective anticancer agents. Graphical abstract   A new and more effective anticancer agent of xanthone derivatives against the oral human epidermoid carcinoma (KB) cell line, as investigated by CoMFA and CoMSIA analysis     

CoMFA and CoMSIA studies on a new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line

Abstract  

A new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line is examined to determine the relationship between the structural properties and the biological activity of these compounds—the 3-D quantitative structure–activity relationship (3D-QSAR)—using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA and CoMSIA models were obtained using the atom-based alignment of 33 compounds, 22 training compounds and 11 tested compounds, and these give desirable statistics; those for the CoMFA standard model were: r _cv² = 0.691, r ² = 0.998, S _press = 0.178, s = 0.014 and F = 1080.765, while CoMSIA combined steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields: r _cv² = 0.600, r ² = 0.988, S _press = 0.206, s = 0.034 and F = 284.433. The 3D-QSAR models calculated satisfactory test set activities. The 3D-QSAR contour plots correlated strongly with the experimental data for the binding topology. For this reason, these results would be beneficial for predicting affinities with the compounds of interest, and they are advantageous for guiding the design and synthesis of new and more effective anticancer agents.     

3D-QSAR study,docking molecular and simulation dynamic on series of benzimidazole derivatives as anti-cancer agents

Benzimidazole is an important heterocyclic organic compound which has a structural analogy to nucleotides found in human body and hence is an important pharmacophore in medicinal chemistry. The anti-cancer activities for a diverse set of benzimidazole as anti-cancer agents against breast cancer cell line (MCF7) assay have been subjected to 3D-QSAR (3-Dimensional Quantitative Structural-Activity Relationship) studies. Both CoMFA and CoMSIA models exhibit significant results in terms of statistical parameters as determination coefficients R² > 0.9 and Leave One Out cross-validation determination coefficients Q²> 6. The predictive quality of both 3D QSAR models have been assessed by external validation and Y-randomization test. Five new compounds have been designed and predicted by in silico ADMET method. In the second part, we have used the docking molecular and simulation dynamics (MD) to investigate the bonding interactions and stability of the designed compounds into the Pin1. Then, we have compared them to Trastuzumab and Tamoxifen as a standard inhibitors drug of breast cancer. The designed compounds form stable hydrogen and hydrophobic bonding interactions with the residues Lys63, Gln131, Ser154, Arg 68 and Arg69 of Pin1 receptor during 100 ns as a time of the simulation. The obtained results showed that the new benzimidazole are useful as a template for future design of more potent inhibitors against breast cancer cell lines (MCF7).     

Molecular modeling of triazine type MDR modulators using CoMFA and CoMSIA approaches

In the present study a series of 30 triazine derivatives was investigated by 3D QSAR methods with respect to their MDR reversing activity in vitro . Two approaches were applied and compared: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Molecular models with good predictive power were derived using steric, electrostatic and hydrophobic fields of the compounds. The results indicated the dominant role of the electrostatic and hydrophobic fields for MDR reversing activity of the investigated modulators. The obtained statistical parameters ( Q cv 2 , Q pr 2 ) showed that the CoMFA and CoMSIA models have similar predictivity. The CoMSIA models were slightly better than the CoMFA ones and obtained with lower number of principal components. The models were graphically interpreted using CoMFA and CoMSIA contour plots. The structural regions responsible for the differences in anti-MDR activity were analyzed in respect to their electrostatic and hydrophobic nature. An easier interpretation of the CoMSIA contour plots was noticed.     

Use of 3D QSAR to investigate the mode of binding of pyrazinones to HIV-1 RT

Abstract  

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked conformation were performed for 24 pyrazinone derivatives. All compounds were docked into the wild-type HIV-1 RT binding pocket and the lowest-energy docked configurations were used to construct the 3D QSAR models. The CoMFA and CoMSIA models enable good prediction of inhibition by the pyrazinones, with r_\textcv² r_{\text{cv}}^{2}  = 0.703 and 0.735. Results obtained from CoMFA and CoMSIA based on the docking conformation of the pyrazinones are, therefore, powerful means of elucidating the mode of binding of pyrazinones and suggesting the design of new potent NNRTIs.     

香水百合香气成分的气相色谱保留指数三维定量构效关系研究

采用比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）方法,研究了香水百合中38种香气成分分子结构与气相色谱保留指数值之间的定量构效关系。用外部测试集验证法和留一交叉验证法对模型的稳健性和预测能力进行了检验,并通过CoMSIA模型和CoMFA模型的分子场三维等势图研究了这些化合物分子中不同化学结构对保留指数值的影响。检验结果表明,所建立的CoMSIA模型和CoMFA模型都具有较好的稳健性和预测能力,且能够合理解释结构对保留指数值的影响,可应用于对香水百合香气成分的色谱保留指数值的预测。与CoMFA模型相比,CoMSIA模型的预测准确度更高,在香水百合香气成分的色谱定量构效关系研究中,显然有更好的应用前景。     

Molecular modeling studies on 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor

A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of the 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor. The CoMFA and CoMSIA models resulting from a 21 molecule training set gave r2(cv) values of 0.631 and 0.542 and r2 values of 0.986 and 0.981, respectively. The statistically significant models were validated by a test set of five compounds with predictive r2(pred). values of 0.967 and 0.981 for CoMFA and CoMSIA, respectively. A systemic external validation was also performed on the established models. The information obtained from 3D counter maps could facilitate the design of more potent human TRPA1 receptor agonists.     

3D QSAR Modeling and Molecular Docking Studies on a Series of Triazole Analogues as Antibacterial Agents

The 3D QSAR analysis using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques is performed on novel nalidixic acid based 1,2,4-triazole derivatives suggested earlier as antibacterial agents. The CoMFA and CoMSIA models employed for a training set of 28 compounds gives reliable values of Q² (0.53 and 0.52, respectively) and R² (0.79 and 0.85, respectively). The contour maps produced by the CoMFA and CoMSIA models are used to determine a three-dimensional quantitative structure-activity relationship. Based on the 3D QSAR contours new molecules with high predicted activities are designed. In addition, surflex-docking is performed to confirm the stability of predicted molecules in the receptor.     

Studies on a Series of Coumarin Derivatives for Anticancer Activity against Breast Carcinoma Cell Line MCF-7 and Their Molecular Design

Breast cancer is one of the most common malignant tumors in women, and is also the focus of researchers. In this article, 3D-QSAR(three-dimensional quantitative structure-activity relationship) was performed on 24 molecules which are a series of coumarin derivatives for their anticancer activity. Our team divided these compounds randomly into the training and test sets to build the CoMFA(comparative molecular field analysis) and CoMSIA(comparative molecular similarity index analysis) models. The coefficients of cross-validation Q~2 and non cross-validation R~2 for CoMFA model were 0.684 and 0.949, and 0.579 and 0.930 for the CoMSIA model, respectively. The result demonstrates that the model has strong stability and satisfactory predictability. 3D contour maps suggest that the electrostatic factor has the greatest impact on activity followed by the H-bonding acceptor and hydrophilic factors. Taking the above results into account, we designed several molecules with high anticancer activity against breast carcinoma cell line MCF-7.     

QM/MM based 3D QSAR models for potent B-Raf inhibitors

Three dimensional (3D) quantitative structure-activity relationship studies of 37 B-Raf inhibitors, pyrazole-based derivatives, were performed. Based on the co-crystallized compound (PDB ID: 3D4Q), several alignment methods were utilized to derive reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. Receptor-guided alignment with quantum mechanics/molecular mechanics (QM/MM) minimization led to the best CoMFA model (q ² = 0.624, r ² = 0.959). With the same alignment, a statistically reliable CoMSIA model with steric, H-bond acceptor, and hydrophobic fields was also derived (q ² = 0.590, r ² = 0.922). Both models were validated with an external test set, which gave satisfactory predictive r ² values of 0.926 and 0.878, respectively. Contour maps from CoMFA and CoMSIA models revealed important structural features responsible for increasing biological activity within the active site and explained the correlation between biological activity and receptor-ligand interactions. New fragments were identified as building blocks which can replace R1-3 groups through combinatorial screening methods. By combining these fragments a compound with a high bioactivity level prediction was found. These results can offer useful information for the design of new B-Raf inhibitors.     

3D QSAR and molecular docking studies of benzimidazole derivatives as hepatitis C virus NS5B polymerase inhibitors

The urgent need for novel HCV antiviral agents has provided an impetus for understanding the structural requisites of NS5B polymerase inhibitors at the molecular level. Toward this objective, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of 67 HCV NS5B polymerase inhibitors were performed using two methods. First, ligand-based 3D QSAR studies were performed based on the lowest energy conformations employing the atom fit alignment method. Second, receptor-based 3D QSAR models were derived from the predicted binding conformations obtained by docking all NS5B inhibitors at the allosteric binding site of NS5B (PDB ID: 2dxs). Results generated from the ligand-based model were found superior (r2cv values of 0.630 for CoMFA and 0.668 for CoMSIA) to those obtained by the receptor-based model (r2cv values of 0.536 and 0.561 for CoMFA and CoMSIA, respectively). The predictive ability of the models was validated using a structurally diversified test set of 22 compounds that had not been included in a preliminary training set of 45 compounds. The predictive r2 values for the ligand-based CoMFA and CoMSIA models were 0.734 and 0.800, respectively, while the corresponding predictive r2 values for the receptor-based CoMFA and CoMSIA models were 0.538 and 0.639, respectively. The greater potency of the tryptophan derivatives over that of the tyrosine derivatives was interpreted based on CoMFA steric and electrostatic contour maps. The CoMSIA results revealed that for a NS5B inhibitor to have appreciable inhibitory activity it requires hydrogen bond donor and acceptor groups at the 5-position of the indole ring and an R substituent at the chiral carbon, respectively. Interpretation of the CoMFA and CoMSIA contour maps in context of the topology of the allosteric binding site of NS5B provided insight into NS5B-inhibitor interactions. Taken together, the present 3D QSAR models were found to accurately predict the HCV NS5B polymerase inhibitory activity of structurally diverse test set compounds and to yield reliable clues for further optimization of the benzimidazole derivatives in the data set.     

新型三唑类抗真菌化合物的三维定量构效关系研究

采用比较分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 系统研究了40个新型三唑类化合物抗真菌活性的三维定量构效关系. 在CoMFA研究中, 研究了两种药效构象对模型的影响, 并考察了网格点步长对统计结果的影响. 在CoMSIA研究中, 系统考察了各种分子场组合、网格点步长和衰减因子对模型统计结果的影响, 发现立体场、静电场、疏水场和氢键受体场的组合得到最佳模型. 所建立CoMFA和CoMSIA模型的交叉相关系数q²值分别为0.718和0.655, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系, 阐明了化合物结构中苯环上各位置取代基对抗真菌活性的影响, 为进一步结构优化提供了重要依据.     

Three-dimensional Quantitative Structure-Activity Relationship Analyses of a Series of Butenolide ETA Antagonists

Two kinds of Three-dimensional Quantitative Structure-activity Relationship(3D-QSAR) methods,comparative molecular filed analysis(CoMFA) and comparative molecular similarity indices analysis (CoMSIA) ,were applied to analyze the structure-activity relationship of a series of 63 butenolide ETA selective antagonists with respect to their inhibition against human ETA receptor,The CoMFA and CoMSIA models were developed for the conceivable alignment of the molecules based on a template structure from the crystallized data.The statistical results from the initial orientation of the aligned molecules show that the 3D-QSAR model from CoMFA(q^2=0.543) is obviously superior to that from the conventional CoMSIA(q^2=0.407).In order to refine the model,all-space search (ASS) was applied to minimize the field sampling process.By rotating and translating the molecular aggregate within the grid systematically,all the possible samplings of the molecular fields were tested and subsequently the one with the highest q^2 was picked out .The comparison of the sensitivity of CoMFA and CoMSIA to different space orientation shows that the CoMFA q^2 values are more sensitive to the translations and rotations of the aligned molecules with respect to the lattice than those of CoMSIA.The best CoMFA model from ASS was further refined by the region focused technique.The high quality of the best model is indicated by the high corss-validated correlation and the prediction on the external test set.The CoMFA coefficient contour plots identify several key features that explain the wide range of activities,which may help us to design new effective ETA selective antagonists.     

Molecular docking and 3D-quantitative structure activity relationship analyses of peptidyl vinyl sulfones: <Emphasis Type="Italic">Plasmodium Falciparum</Emphasis> cysteine proteases inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were conducted on a series (39 molecules) of peptidyl vinyl sulfone derivatives as potential Plasmodium Falciparum cysteine proteases inhibitors. Two different methods of alignment were employed: (i) a receptor-docked alignment derived from the structure-based docking algorithm GOLD and (ii) a ligand-based alignment using the structure of one of the ligands derived from a crystal structure from the PDB databank. The best predictions were obtained for the receptor-docked alignment with a CoMFA standard model (q ² = 0.696 and r ² = 0.980) and with CoMSIA combined electrostatic, and hydrophobic fields (q ² = 0.711 and r ² = 0.992). Both models were validated by a test set of nine compounds and gave satisfactory predictive r ² _pred values of 0.76 and 0.74, respectively. CoMFA and CoMSIA contour maps were used to identify critical regions where any change in the steric, electrostatic, and hydrophobic fields may affect the inhibitory activity, and to highlight the key structural features required for biological activity. Moreover, the results obtained from 3D-QSAR analyses were superimposed on the Plasmodium Falciparum cysteine proteases active site and the main interactions were studied. The present work provides extremely useful guidelines for future structural modifications of this class of compounds towards the development of superior antimalarials.     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.     

Molecular modeling of triazine type MDR modulators using CoMFA and CoMSIA approaches

In the present study a series of 30 triazine derivatives was investigated by 3D QSAR methods with respect to their MDR reversing activity in vitro. Two approaches were applied and compared: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Molecular models with good predictive power were derived using steric, electrostatic and hydrophobic fields of the compounds. The results indicated the dominant role of the electrostatic and hydrophobic fields for MDR reversing activity of the investigated modulators. The obtained statistical parameters (Qcv2, Qpr2) showed that the CoMFA and CoMSIA models have similar predictivity. The CoMSIA models were slightly better than the CoMFA ones and obtained with lower number of principal components. The models were graphically interpreted using CoMFA and CoMSIA contour plots. The structural regions responsible for the differences in anti-MDR activity were analyzed in respect to their electrostatic and hydrophobic nature. An easier interpretation of the CoMSIA contour plots was noticed.     

Studies on molecular mechanism between SHP2 and pyridine derivatives by 3D-QSAR,molecular docking and MD simulations

BackgroundSrc homology 2 (SH2)-containing protein tyrosine phosphatase 2 (SHP2) as a major phosphatase would affect the development of tumors by regulating several cellular processes, and is a significant potential target for cancer treatment.MethodsIn the present work, a series of pyridine derivatives possessing a wide range of inhibitory activity was employed to investigate the structural requirements by developing three dimensional quantitative structure–activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The results show that CoMFA (R²_cv = 0.646, R²_pred = 0.5587) and CoMSIA (R²_cv = 0.777, R²_pred = 0.7131) have excellent stability and predictability. The relationship between the inhibitory activity and structure of the inhibitors was analyzed by the derived contour maps. Furthermore, the QSAR models were validated by molecular docking and molecular dynamics simulations, which were also applied to reveal the potential molecular mechanism of these inhibitors.FindingsIt was found that Arg110, Asn216, Thr218, Thr252 and Pro490 play a crucial role in stabilizing the inhibitors. Additionally, MM/PBSA calculations provided the binding free energy were also conducted to explain the discrepancy of binding activities. Overall, the outcomes of this work could provide useful information and theoretical guidance for the development of novel and potent SHP2 inhibitors.     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.