Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors

Summary Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(1/K_i), with an explained variance r² of 0.885, and a cross-validated q² of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q² for one component was 0.62 and 0.61, respectively, while r² was 0.674. We demonstrate that the predictive r² based on the mean activity (Y_m) of the training set is misleading, while the test set Y_m-based predictive r² index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substitutents, and a smaller one at the C6-beta region.     

Three-dimensional quantitative structure-activity relationship of nucleosides acting at the A3 adenosine receptor: analysis of binding and relative efficacy

The binding affinity and relative maximal efficacy of human A3 adenosine receptor (AR) agonists were each subjected to ligand-based three-dimensional quantitative structure-activity relationship analysis. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) used as training sets a series of 91 structurally diverse adenosine analogues with modifications at the N6 and C2 positions of the adenine ring and at the 3', 4', and 5' positions of the ribose moiety. The CoMFA and CoMSIA models yielded significant cross-validated q2 values of 0.53 (r2 = 0.92) and 0.59 (r2 = 0.92), respectively, and were further validated by an external test set (25 adenosine derivatives), resulting in the best predictive r2 values of 0.84 and 0.70 in each model. Both the CoMFA and the CoMSIA maps for steric or hydrophobic, electrostatic, and hydrogen-bonding interactions well reflected the nature of the putative binding site previously obtained by molecular docking. A conformationally restricted bulky group at the N6 or C2 position of the adenine ring and a hydrophilic and/or H-bonding group at the 5' position were predicted to increase A3AR binding affinity. A small hydrophobic group at N6 promotes receptor activation. A hydrophilic and hydrogen-bonding moiety at the 5' position appears to contribute to the receptor activation process, associated with the conformational change of transmembrane domains 5, 6, and 7. The 3D-CoMFA/CoMSIA model correlates well with previous receptor-docking results, current data of A3AR agonists, and the successful conversion of the A3AR agonist into antagonists by substitution (at N6) or conformational constraint (at 5'-N-methyluronamide).     

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.     

楝酰胺(Rocaglamide)类化合物的三维定量构效关系

采用比较分子力场分析(CoMFA)和比较分子相似因子分析(CoMSIA)方法,对训练集中的26个楝酰胺(Rocaglamide)类化合物进行了三维定量构效关系(3D-QSAR)研究,最终建立的CoMFA模型和CoMSlA模型的q<'2>分别为0.593和0.656.并对测试集中的5个化合物的生物活性进行了预测,结果表明...     

3D-QSAR and Docking Studies of Pyrido[2,3-d]pyrimidine Derivatives as Wee1 Inhibitors

In order to investigate the inhibiting mechanism and obtain some helpful information for de-signing functional inhibitors against Wee1, three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies have been performed on 45 pyrido[2,3-d] pyrim-idine derivatives acting as Wee1 inhibitors. Two optimal 3D-QSAR models with significant statistical quality and satisfactory predictive ability were established, including the CoMFA model (q²=0.707, R²=0.964) and CoMSIA model (q²=0.645, R²=0.972). The external val-idation indicated that both CoMFA and CoMSIA models were quite robust and had high predictive power with the predictive correlation coefficient values of 0.707 and 0.794, essen-tial parameter r²_m values of 0.792 and 0.826, the leave-one-out r²_m(LOO) values of 0.781 and 0.809, r²_m(overall) values of 0.787 and 0.810, respectively. Moreover, the appropriate binding orientations and conformations of these compounds interacting with Wee1 were revealed by the docking studies. Based on the CoMFA and CoMSIA contour maps and docking analyses, several key structural requirements of these compounds responsible for inhibitory activity were identified as follows: simultaneously introducing high electropositive groups to the sub-stituents R1 and R5 may increase the activity, the substituent R2 should be smaller bulky and higher electronegative, moderate-size and strong electron-withdrawing groups for the substituent R3 is advantageous to the activity, but the substituent X should be medium-size and hydrophilic. These theoretical results help to understand the action mechanism and design novel potential Wee1 inhibitors.     

含呋喃环双酰脲类衍生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), 对27个新型双酰基脲类化合物的杀蚊幼虫(Aedes aegypti L.)活性进行三维定量构效关系(3D-QSAR)研究. 在CoMFA研究中, 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, 发现立体场和氢键供体场的组合得到最佳模型. 所建立的CoMFA和CoMSIA模型的非交叉验证相关系数r2值分别为0.828和0.841, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值图不仅直观地解释了结构与活性的关系, 而且为后续优化该系列化合物提供了理论依据.     

CoMFA-based comparison of two models of binding site on adenosine A1 receptor

A set of 32 N6-substituted adenosines and 22 8-substituted xanthines with affinity for adenosine A1 receptors was subjected to three-dimensional quantitative structure-affinity relationship analysis using comparative molecular field analysis (CoMFA). The aim was to compare two modes of binding to the receptor – `N6-C8' and `N6-N7'. Good models with high predictive power and stability were obtained. A comparison of these models gives the following results: (a) Inclusion of both steric and electrostatic fields in CoMFA generates better predictive models compared to models based on steric or electrostatic fields alone. (b) The `N6-N7' CoMFA models are slightly better than the `N6-C8' ones. (c) Steric restriction exists around the N6-H in the `N6-N7' steric field map, which is absent in the `N6-C8' steric field map. This report demonstrates that the `N6-N7' mode of binding is a further development of the `N6-C8' model with a slightly better predictive ability and more accurate steric and electrostatic overlaps between agonists and antagonists.     

3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q² = 0.589 and r² = 0.927 and q² = 0.473 and r² = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 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.     

3D-QSAR study of hallucinogenic phenylalkylamines by using CoMFA approach

The three-dimensional quantitative structure–activity relationship (3D-QSAR) has been studied on 90 hallucinogenic phenylalkylamines by the comparative molecular field analysis (CoMFA). Two conformations were compared during the modeling. Conformation I referred to the amino group close to ring position 6 and conformation II related to the amino group trans to the phenyl ring. Satisfactory results were obtained by using both conformations. There were still differences between the two models. The model based on conformation I got better statistical results than the one about conformation II. And this may suggest that conformation I be preponderant when the hallucinogenic phenylalkylamines interact with the receptor. To further confirm the predictive capability of the CoMFA model, 18 compounds with conformation I were randomly selected as a test set and the remaining ones as training set. The best CoMFA model based on the training set had a cross-validation coefficient q ² of 0.549 at five components and non cross-validation coefficient R ² of 0.835, the standard error of estimation was 0.219. The model showed good predictive ability in the external test with a coefficient R _pre² of 0.611. The CoMFA coefficient contour maps suggested that both steric and electrostatic interactions play an important role. The contributions from the steric and electrostatic fields were 0.450 and 0.550, respectively.     

A ligand-based comparative molecular field analysis (CoMFA) and homology model based molecular docking studies on 3′, 4′-dihydroxyflavones as rat 5-lipoxygenase inhibitors: Design of new inhibitors

In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3′, 4′-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R² values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q² values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed.     

Insight into the structural requirements of narlaprevir-type inhibitors of NS3/NS4A protease based on HQSAR and molecular field analyses

In the life cycle of hepatitis C virus (HCV), NS3/NS4A protease has been proved to play a vital role in the replication of the HCV virus. Narlaprevir and its derivatives, the inhibitors of NS3/NS4A, would be potentially developed as important anti-HCV drugs in the future. In this study, quantitative structure-activity relationship (QSAR) analyses for 190 narlaprevir derivatives were conducted using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) techniques. Both of the best CoMFA and HQSAR models showed statistical significance for the training set and good predictive accuracy for the test set, which strongly manifested the robustness of the CoMFA and HQSAR models. The CoMFA contour maps and the HQSAR contribution maps were both presented. Furthermore, based on the essential factors for ligand binding derived from the QSAR models, sixteen new derivatives were designed and some of them showed higher inhibitory activities confirmed by our models and molecular docking studies. General speaking, this study provides useful suggestions for the design of potential anti-HCV drugs.     

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.     

Insight into the structural requirements of benzimidazole derivatives as interleukin‐2 inducible t‐cell kinase inhibitors by computational explorations

In the present work, a set of ligand‐ and receptor‐based 3D‐QSAR models were developed to explore the structure–activity relationship of 109 benzimidazole‐based interleukin‐2‐inducible T‐cell kinase (ITK) inhibitors. In order to reveal the requisite 3D structural features impacting the biological activities, a variety of in silico modeling approaches including the comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), docking, and molecular dynamics were applied. The results showed that the ligand‐based CoMFA model (Q² = 0.552, R²_ncv = 0.908, R²_pred = 0.787, SEE = 0.252, SEP = 0.558) and CoMSIA model (Q² = 0.579, R²_ncv = 0.914, R²_pred = 0.893, SEE = 0.240, SEP = 0.538) were superior to other models with greater predictive power. In addition, a combined analysis between the 3D contour maps and docking results showed that: (1) Compounds with bulky or hydrophobic substituents near ring D and electropositive or hydrogen acceptor groups around rings C and D could increase the activity. (2) The key amino acids impacting the receptor–ligand interactions in the binding pocket are Met438, Asp500, Lys391, and Glu439. The results obtained from this work may provide helpful guidelines in design of novel benzimidazole analogs as inhibitors of ITK. © 2013 Wiley Periodicals, Inc.     

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of thiazolone derivatives as hepatitis C virus NS5B polymerase allosteric inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a series of thiazolone derivatives as novel inhibitors bound to the allosteric site of hepatitis C virus (HCV) NS5B polymerase were developed based on CoMFA and CoMSIA analyses. Two different conformations of the template molecule and the combinations of different CoMSIA field/fields were considered to build predictive CoMFA and CoMSIA models. The CoMFA and CoMSIA models with best predictive ability were obtained by the use of the template conformation from X-ray crystal structures. The best CoMFA and CoMSIA models gave q (2) values of 0.621 and 0.685, and r (2) values of 0.950 and 0.940, respectively for the 51 compounds in the training set. The predictive ability of the two models was also validated by using a test set of 16 compounds which gave r (pred) (2) values of 0.685 and 0.822, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps enables the interpretation of their structure-activity relationship and was also used to the design of several new inhibitors with improved activity.     

3D-QSAR studies and molecular docking on [5-(4-amino-1<Emphasis Type="Italic">H</Emphasis>-benzoimidazol-2-yl)-furan-2-yl]-phosphonic acid derivatives as fructose-1,6-biphophatase inhibitors

Fructose-1,6-biphophatase has been regarded as a novel therapeutic target for the treatment of type 2 diabetes mellitus (T2DM). 3D-QSAR and docking studies were performed on a series of [5-(4-amino-1H-benzoimidazol-2-yl)-furan-2-yl]-phosphonic acid derivatives as fructose-1,6-biphophatase inhibitors. The CoMFA and CoMSIA models using thirty-seven molecules in the training set gave r _cv² values of 0.614 and 0.598, r ² values of 0.950 and 0.928, respectively. The external validation indicated that our CoMFA and CoMSIA models possessed high predictive powers with r ₀² values of 0.994 and 0.994, r _m² values of 0.751 and 0.690, respectively. Molecular docking studies revealed that a phosphonic group was essential for binding to the receptor, and some key features were also identified. A set of forty 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.