3D-QSAR Analysis of a Series of Dihydroquinolizinone Derivatives as a Hepatitis B Virus Expression Inhibitor

In this study, we explored a three-dimensional quantitative structure-activity relationship(3D-QSAR) model of 63 HBV viral gene expression inhibitors containing dihydroquinolizinones. Two high predictive QSAR models have been built, including comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). The internal validation parameter(CoMFA, q~2 = 0.701, r~2 = 0.999; CoMSIA, q~2 = 0.721, r~2 = 0.998) and external validation parameter(CoMFA, r~2_(pred = 0.999); CoMSIA, r~2_(pred = 0.999)) indicated that the models have good predictive abilities and significant statistical reliability. We designed several molecules with potentially higher predicted activity on the basis of the result of the models. This work might provide useful information to design novel HBV viral gene expression inhibitors.     

HLA-A*0201限制性CTL表位肽的三维定量构效关系的研究

运用比较分子力场(CoMFA)和比较分子相似性指数分析(CoMFA)方法研究了50个HLA-A^*0201限制性CTL表位九肽结构与亲和性间的关系,另外15个表位九肽作为预测集用于检验模型的预测能力.结果表明采用CoMSIA得到的构效关系模型(q^2=0.628,r^2=0.997,F=840.419)要明显优于采用CoMFA得到的构效关系模型.在CoMSIA计算中,当引入疏水场时,三维构效关系模型得到明显改善,通过该三维构效关系模型,可较精确地估算预测集中15个CTL表位肽与HLA-A^*0201间的亲和力(r^2pred=0.743).通过分析分子场等值面图在空间的分布,可以观察到表位肽分子周围的立体及疏水特征对表位肽与HLA-A^*0201间结合亲和力的影响,从而为进一步对CTL表位肽进行结构改造并基于此进行治疗性疫苗分子设计提供理论基础.     

Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation

An unusually large data set of 397 piperazinyl-glutamate-pyridines/pyrimidines as potent orally bioavailable P2Y(12) antagonists for inhibition of platelet aggregation was studied for the first time based on the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) methods. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) studies have been performed with a training set of 317 compounds, estimating three superimposition methods. The best CoMFA and CoMSIA models, derived from superimposition I, shows leave-one-out cross-validation correlation coefficients (Q(2)) of 0.571 and 0.592 as well as the conventional correlation coefficients (R(2)(ncv)) of 0.814 and 0.834, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10-fold cross-validation, further indicate the highly statistical significance of the optimal models. The external predictive abilities of these models were evaluated using a prediction set of 80 compounds, producing the predicted correlation coefficients (R(2)(pred)) of 0.664 and 0.668, respectively. The key amino acid residues were identified by molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good concordance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the rational modification of molecules in order to design more potent P2Y(12) antagonists. We hope the developed models could provide some instructions for further synthesis of highly potent P2Y(12) antagonists.     

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.     

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.     

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--property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): enthalpy of sublimation

Three-dimensional quantitative structure--property relationship (3D-QSPR) models have been constructed using comparative molecular field analysis (CoMFA) to correlate the sublimation enthalpies at 298.15 K of a series of polychlorinated biphenyls (PCBs) with their CoMFA-calculated physicochemical properties. Various alignment schemes, such as atom fit, as is, and inertial were employed in this study. Separate CoMFA models were developed using different partial charge formalisms, namely, electrostatic potential (ESP) and Gasteiger-Marsili (GM) charges. Among the four different CoMFA models constructed for sublimation enthalpy (Delta(sub)H(m)(298.15 K)), the model that combined atom fit alignment and ESP charges yielded the greatest self-consistency (r(2) = 0.976) and internal predictive ability (r(cv)(2) = 0.750). This CoMFA model was used to predict Delta(sub)H(m)(298.15 K) of PCBs for which the corresponding experimental values are unavailable in the literature.     

QSAR models using a large diverse set of estrogens

Endocrine disruptors (EDs) have a variety of adverse effects in humans and animals. About 58,000 chemicals, most having little safety data, must be tested in a group of tiered assays. As assays will take years, it is important to develop rapid methods to help in priority setting. For application to large data sets, we have developed an integrated system that contains sequential four phases to predict the ability of chemicals to bind to the estrogen receptor (ER), a prevalent mechanism for estrogenic EDs. Here we report the results of evaluating two types of QSAR models for inclusion in phase III to quantitatively predict chemical binding to the ER. Our data set for the relative binding affinities (RBAs) to the ER consists of 130 chemicals covering a wide range of structural diversity and a 6 orders of magnitude spread of RBAs. CoMFA and HQSAR models were constructed and compared for performance. The CoMFA model had a r2 = 0.91 and a q2LOO = 0.66. HQSAR showed reduced performance compared to CoMFA with r2 = 0.76 and q2LOO = 0.59. A number of parameters were examined to improve the CoMFA model. Of these, a phenol indicator increased the q2LOO to 0.71. When up to 50% of the chemicals were left out in the leave-N-out cross-validation, the q2 remained significant. Finally, the models were tested by using two test sets; the q2pred for these were 0.71 and 0.62, a significant result which demonstrates the utility of the CoMFA model for predicting the RBAs of chemicals not included in the training set. If used in conjunction with phases I and II, which reduced the size of the data set dramatically by eliminating most inactive chemicals, the current CoMFA model (phase III) can be used to predict the RBA of chemicals with sufficient accuracy and to provide quantitative information for priority setting.     

N-硝基脲类化合物除草活性的3D-QSAR研究

经活性测试,N-硝基脲类化合物对反枝苋(A. retroflexus L)和苏丹草(S. sudanenses)呈现除草活性。为进一步设计高活性的目标化合物,采用比较分子力场(CoMFA)对38个N-硝基脲类化合物进行三维定量构效关系(3D-QSAR)分析,建立了相关性显著、预测能力强的3D-QSAR模型(反枝苋：q2=0.674, r2=1.000, R2pred=0.9989,苏丹草：q2=0.635, r2=1.000, R2pred=0.9958)。根据CoMFA模型的立体场和静电场三维等势线图,在N’-苯环2, 5位引入体积大的正电荷取代基;3位引入负电荷基团;4, 6位引入体积大的负电荷基团有利于提高目标化合物对双子叶杂草反枝苋的除草活性,而在2位引入体积大的负电荷基团;3位引入体积小的负电荷基团;4位引入体积大的正电荷基团;5位引入体积大的取代基有利于提高目标化合物对单子叶杂草苏丹草的除草活性。     

磺酰脲类乙酰羟基酸合成酶抑制剂Topomer CoMFA研究

磺酰脲类除草剂是一类高选择性、广谱、低毒的化合物,在世界范围内得到了广泛的应用。本文采用易位体-比较分子力场法（Topomer CoMFA）对75个磺酰脲类化合物与植物源野生型拟南芥AHAS酶的离体相互作用进行了三维定量构效关系研究,快速准确地构建了Topomer CoMFA模型,该模型具有较强的预测能力（交叉验证相关系数q2为0.890,非交叉验证相关系数r2为0.967）。此模型对测试集的10个化合物的pKi值进行预测,其预测值与实际值一致。     

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.     

A comparative study of quantitative structure-activity relationship methods based on gallic acid derivatives

By using hologram quantitative structure-activity relationship (HQSAR) and comparative molecular field analysis (CoMFA) methods, the relationships between the structures of 49 gallic acid derivatives and their analgesic activity have been investigated to yield statistically reliable models with considerable predictive power. The best HQSAR model was generated using atoms, bond and connectivity as fragment distinction parameters and fragment size 5-7 from a hologram length of 307 with 3 components. High conventional r2 (r2 = 0.825) and cross-validation r2 (r2(cv) = 0.726) values were obtained. CoMFA analyses varying lattice size and location, grid spacing, probe charges and using, Tripos standard and Indicator force field were performed. The best model was developed with 4 components using sp3-hybridized carbon atom with +1.0 charge as probe, grid spacing (2 A), lattice offset (1.0, 3.0, -2.5). The CoMFA model showed a conventional correlation coefficient r2 of 0.889 and across-validation r2(cv) equals to 0.633. The robustness and predictive ability of the HQSAR and CoMFA models have been validated by means of an external test set. The results indicate that both models possess high statistical quality in the prediction of analgesic potency of novel gallic acid analogs.     

Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis

Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.     

Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient

The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schu?u?rmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.     

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.     

Three-dimensional quantitative structure-property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): enthalpies of fusion and their application to estimates of enthalpies of sublimation and aqueous solubilities

Comparative Molecular Field Analysis (CoMFA) has been used to develop three-dimensional quantitative structure-property relationship (3D-QSPR) models for the fusion enthalpy at the melting point (Delta(fus)H(m)(T(fus))) of a representative set of polychlorinated biphenyls (PCBs). Various alignment schemes, such as inertial, as is, atom fit, and field fit, were used in this study to evaluate the predictive capabilities of the models. The CoMFA models have also been derived using partial atomic charges calculated from the electrostatic potential (ESP) and Gasteiger-Marsili (GM) methods. The combination of atom fit alignment and GM charges yielded the greatest self-consistency (r(2) = 0.955) and internal predictive ability (r(cv)(2) = 0.783). This CoMFA model was used to predict Delta(fus)H(m)(T(fus)) of the entire set of 209 PCB congeners, including 193 PCB congeners for which experimental values are unavailable. The CoMFA-predicted values, combined with previous estimations of vaporization and sublimation enthalpies, were used to construct a thermodynamic cycle that validated the internal self-consistency of the predictions for these three thermodynamic properties. The CoMFA-predicted values of fusion enthalpy were also used to calculate aqueous solubilities of PCBs using Mobile Order and Disorder Theory. The agreement between calculated and experimental values of solubility at 298.15 K, characterized by a standard deviation of +/- 0.41 log units, demonstrates the utility of CoMFA-predicted values of fusion enthalpies to calculate aqueous solubilities of PCBs.     

CoMFA modeling of enzyme kinetics: K(m) values for sulfation of diverse phenolic substrates by human catecholamine sulfotransferase SULT1A3

Three-dimensional QSAR models were developed for predicting kinetic Michaelis constant (K(m)) values for phenolic substrates of human catecholamine sulfating sulfotransferase (SULT1A3). The K(m) values were correlated to the steric and electronic molecular fields of the substrates utilizing Comparative Molecular Field Analysis (CoMFA). The evaluated SULT1A3 substrate data set consisted of 95 different substituted phenols, catechols, catecholamines, steroids, and related structures for which the K(m) values were available. The data set was divided in three different subgroups in the initial analysis: (1). for the first CoMFA model substrates with only one reacting hydroxyl group were selected (n = 51), (2).the second model was build with structurally rigid substrates (n = 59), and (3). finally all substrates of the data set were included in the analysis (n = 95). Substrate molecules were aligned using the aromatic ring and the reacting hydroxyl group as a template. After the initial analysis different substrate alignment rules based on the existing knowledge of the SULT1A3 active site structure were evaluated. After this optimization a final CoMFA model was built including all 95 substrates of the data set. Cross-validated q(2) values (leave-one-out and leave-n-out) and coefficient contour maps were calculated for all derived CoMFA models. All four CoMFA models were statistically significant with q(2) values up to 0.624. These predictive QSAR models will provide us information about the factors that affect substrate binding at the active site of human catecholamine sulfotransferase SULT1A3.