Three-dimensional Quantitative Structure-activity Relationship Models of HIV-1 Integrase Inhibitors of DKAs

As one of the three viral encoded enzymes of HIV-1 infection, HIV-1 integrase has become an attractive drug target for the treatment. Diketoacid compounds (DKAs) are one kind of potent and selective inhibitors of HIV-1 IN. In the present work, two three-dimensional QSAR techniques (CoMFA and CoMSIA) were employed to correlate the molecular structure with the activity of inhibiting the strand transfer for 147 DKAs. The all-oritation search (AOS) and all-placement search (APS) were used to optimize the CoMFA model. The diketo and keto-enol tautomers of DKAs were also used to establish the CoMFA models. The results indicated that the enol was the dominant conformation in the HIV-1 IN and DKAs complexes. It can provide a new method and reference to identify the bioactive conformation of drugs by using QSAR analysis. The best CoMSIA model, with five fields combined, implied that the hydrophobic field is very important as well as the steric and electrostatic fields. All models indicated favorable internal validation. A comparative analysis with the three models demonstrated that the CoMFA model seems to be more predictive. The contour maps could afford steric, electrostatic, hydrophobic and H-bond information about the interaction of ligand-receptor complex visually. The models would give some useful guidelines for designing novel and potent HIV-1 integrase inhibitors.     

Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.     

苯并咪唑类缓蚀剂的3D-QSAR研究及分子设计

采用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 对苯并咪唑衍生物抗盐酸腐蚀的缓蚀性能进行了三维定量构效关系研究, 并使用留一法交叉验证手段对3D-QSAR模型的稳定性及预测能力进行了分析. 结果表明, 立体场、静电场和氢键供体场(电子给体)是影响苯并咪唑缓蚀剂缓蚀性能的主要因素; 所构建的CoMFA模型(q2=0.541, R2=0.996)和CoMSIA模型(q2=0.581, R2=0.987)均具有较好的统计学稳定性和预测能力. 基于3D-QSAR等势图设计出了几种具有较好缓蚀性能的苯并咪唑化合物, 为油气田新型缓蚀剂的研发提供了一种新思路.     

2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的3D-QSAR研究

用比较分子力场分析(CoMFA)方法和比较分子相似性指数分析(CoMSIA)方法研究了21个2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的三维定量构效关系(3D-QSAR),发现用CoMFA方法可以找到最佳的3D-QSAR模型,并通过量子化学从头计算的方法研究了不同活性化合物的前线轨道及静电势分布图的差异.所得构效关系模型为发现更高活性的化合物提供理论指导.     

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.     

芳香噻嗪类衍生物作为选择性醛糖还原酶抑制剂的分子对接和基于受体的三维定量构效关系研究

芳香噻嗪类衍生物被证明是一类选择性较好的高活性醛糖还原酶抑制剂(ARIs).本文对44个芳香噻嗪类化合物进行了分子对接(docking)和三维定量构效关系(3D-QSAR)研究,并探索了此类化合物与醛糖还原酶(ALr²)的作用机理.醛糖还原酶与醛还原酶(ALR1)活性位点的叠加结果显示, ALr²中残基Leu 300和Cys298的存在是化合物1m具有高选择性的原因.分别建立了比较分子场分析方法(CoMFA, q² = 0.649, r² =0.934; q²:交叉验证相关系数, r²:非交叉验证相关系数)和比较分子相似性指数分析方法(CoMSIA, q² = 0.746, r² = 0.971)模型,并对影响此类化合物生物活性的结构进行了鉴定.结果显示,两个模型均具有较高预测能力,并通过测试集中的7个化合物进行了验证,其中CoMFA模型和CoMSIA模型的预测相关系数(r_Pred²)分别为0.748和0.828. 3D-QSAR模型中的三维等值线图表明,在化合物1m的苄基环上C3和C4位置以及苯并噻嗪母核上C5和C7位置进行改进可能对生物活性的提高有利,此预测与我们前期报道的苯并噻嗪母核C7位改进结果一致.本文所建3D-QSAR模型能够在理性设计具有更高生物活性的新型ARIs中发挥重要作用.     

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.     

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) and Comparative Molecular Field Analysis (CoMFA) studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q² from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and electrostatic interactions played important roles.     

3D-QSAR and molecular docking studies of 4-methyl quinazoline derivatives as PI3Kα inhibitors

A new series of 4- methyl quinazoline derivatives was synthesized and its anti-cancer activity was assessed. It was revealed that its compounds have potent inhibition on related phosphoinositide 3-kinases alpha (PI3Kα). In this study, the three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking approaches were performed on a series of 4-methyl quinazoline derivatives with PI3Kα inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q²) values of 0.850 and 0.92, the determination coefficient (R²) values of 0.998 and 0.987, and the standard error of the estimate (SEE) values of 0.017 and 0.105, respectively. The acceptable values of determination coefficient (R² test) to CoMFA and CoMSIA respectively corresponding to values of 0.793 and 0.804 utilizing a test set of seven molecules prove the high predictive ability of this model. Using AutoDock tools, Molecular docking analysis was utilized to validate 3D-QSAR methods and to explain the binding site interactions and energy between the most active ligands and the PI3Kα (PDB ID: 4JPS) receptor. Based on these results, a novel series of 4- methyl quinazoline derivatives was predicted.     

A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study using Comparative Molecular Similarity Indices Analysis (CoMSIA) was conducted on a series of 3-azolylmethylindoles as anti-leishmanial agents. Evaluation of 24 compounds synthesized in our laboratory served to establish the model. A random search was performed on the library of compounds, and molecules of the training set were aligned on common elements of template molecule 13, one of the most active compounds. The best predictions were obtained from multifit procedure with a CoMSIA model combining steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (q2 = 0.594, r2 = 0.897). The model was validated using an external test set of 7 compounds giving a satisfactory predictive r2 value of 0.649. Information obtained from CoMSIA contour maps could be used for further design of more promising inhibitors.     

人类免疫缺陷病毒整合酶二酮酸类抑制剂的三维药效团构建

应用遗传算法相似性程序(GASP), 以作用于I型人类免疫缺陷病毒(human immun-odeficiency virus type 1, HIV-1)整合酶(IN)的二酮酸类(diketoacids, DKAs)抑制剂构建药效团模型. 所选训练集分子均具有可靠的类药性特征及DKAs药效团特征. 尝试将抑制剂与药效团叠合后的构象和抑制剂与IN的对接构象进行叠合, 得到药效团模型与分子对接构象中IN残基的相对位置, 并基于抑制剂的药效团模型特征与周围IN氨基酸残基位置的匹配情况进行药效团特征的修改. 所得最优药效团由1个疏水特征、3对氢键特征和1个氢键供体特征组成. 该药效团的命中物质量(goodness of hit, GH)为0.56, 产出率(Y)达63.6%, 假阳性率(FP)为0.41%. 该药效团具有较好的置信度, 产出率较高而假阳性率较低, 可用于数据库搜索发现新的具有DKAs药效团特征的活性化合物, 也可为先导化合物的改造提供帮助.     

Prediction of Stability for Polychlorinated Biphenyls in Transformer Insulation Oil Through Three-dimensional Quantitative Structure-activity Relationship Pharmacophore Model and Full Factor Experimental Design

Based on the obtained data of half-lives(t_1/2) for 31 polychlorinated biphenyl congeners(PCBs), 3D quantitative structure-activity relationship(QSAR) pharmacophore was used to establish a 3D QSAR model to predict the t_1/2 values of the remaining 178 PCBs, using the structural parameters as independent variables and lgt_1/2 values as the dependent variable. Among this process, the whole data set(31 compounds) was divided into a training set(24 compounds) for model generation and a test set(7 compounds) for model validation. Then, the full factor experimental design was used to research the potential second-order interactional effect between different substituent positions, obtaining the final regulation scheme for PCB. At last, a 3D QSAR pharmacophore model was established to validate the reasonable regulation targeting typical PCB with respect to half-lives and thermostability. As a result, the cross-validation correlation coefficient(q²) obtained by the 3D QSAR model was 0.845(>0.5) and the coefficient of determination(r²) obtained was 0.936(>0.9), indicating that the models were robust and predictive. CoMSIA analyses upon steric, electrostatic and hydrophobic fields were 0.7%, 85.9%, and 13.4%, respectively. The electrostatic field was determined to be a primary factor governing the t_1/2. From CoMSIA contour maps, t_1/2 increased when substi- tuents possessed electropositive groups at the 2'-, 3-, 3'-, 5- and 5'- positions and electronegative groups at the 3-, 3'-, 5-, 6- and 6'- positions, which could increase the PCB stability in transformer insulation oil. Modification of two typical PCB congeners(PCB-77 and PCB-81) showed that the lgt_1/2for three selected modified compounds increased by 13%(average ratio) compared with that of each congener and the thermostability of them were higher, validating the reasonability of the regulatory scheme obtained from the 3D QSAR model. These results are expected to be beneficial in predicting t_1/2 values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the stability of PCBs.     

Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer

Microtubules are tube-shaped, filamentous and cytoskeletal proteins that are essential in all eukaryotic cells. Microtubule is an attractive and promising target for anticancer agents. In this study, three-dimensional quantitative structure activity relationships (3D-QSAR) including comparative molecular field analysis, CoMFA, and comparative molecular similarity indices analysis, CoMSIA, were performed on a set of 45 (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule-targeted anti-prostate cancer agents. Automated grid potential analysis, AutoGPA module in Molecular Operating Environment 2009.10 (MOE) as a new 3D-QSAR approach with the pharmacophore-based alignment was carried out on the same dataset. AutoGPA-based 3D-QSAR model yielded better prediction parameters than CoMFA and CoMSIA. Based on the contour maps generated from the models, some key features were identified in (E)-N-Aryl-2-arylethene-sulfonamide analogues that were responsible for the anti-cancer activity. Virtual screening was performed based on pharmacophore modeling and molecular docking to identify the new inhibitors from ZINC database. Seven top ranked compounds were found based on Gold score fitness function. In silico ADMET studies were performed on compounds retrieved from virtual screening in compliance with the standard ranges.     

Mapping the binding site of a large set of quinazoline type EGF-R inhibitors using molecular field analyses and molecular docking studies

In the current work, three-dimensional QSAR studies for one large set of quinazoline type epidermal growth factor receptor (EGF-R) inhibitors were conducted using two types of molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). These compounds belonging to six different structural classes were randomly divided into a training set of 122 compounds and a test set of 13 compounds. The statistical results showed that the 3D-QSAR models derived from CoMFA were superior to those generated from CoMSIA. The most optimal CoMFA model after region focusing bears significant cross-validated r(2)(cv) of 0.60 and conventional r(2) of 0.92. The predictive power of the best CoMFA model was further validated by the accurate estimation to these compounds in the external test set, and the mean agreement of experimental and predicted log(IC(50)) values of the inhibitors is 0.6 log unit. Separate CoMFA models were conducted to evaluate the influence of different partial charges (Gasteiger-Marsili, Gasteiger-Hückel, MMFF94, ESP-AM1, and MPA-AM1) on the statistical quality of the models. The resulting CoMFA field map provides information on the geometry of the binding site cavity and the relative weights of various properties in different site pockets for each of the substrates considered. Moreover, in the current work, we applied MD simulations combined with MM/PBSA (Molecular mechanics/Possion-Boltzmann Surface Area) to determine the correct binding mode of the best inhibitor for which no ligand-protein crystal structure was present. To proceed, we define the following procedure: three hundred picosecond molecular dynamics simulations were first performed for the four binding modes suggested by DOCK 4.0 and manual docking, and then MM/PBSA was carried out for the collected snapshots. The most favorable binding mode identified by MM/PBSA has a binding free energy about 10 kcal/mol more favorable than the second best one. The most favorable binding mode identified by MM/PBSA can give satisfactory explanation of the SAR data of the studied molecules and is in good agreement with the contour maps of CoMFA. The most favorable binding mode suggests that with the quinazoline-based inhibitor, the N3 atom is hydrogen-bonded to a water molecule which, in turn, interacts with Thr 766, not Thr 830 as proposed by Wissner et al. (J. Med. Chem. 2000, 43, 3244). The predicted complex structure of quinazoline type inhibitor with EGF-R as well as the pharmacophore mapping from CoMFA can interpret the structure activities of the inhibitors well and afford us important information for structure-based drug design.     

Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

A ligand-based molecular modeling study on some matrix metalloproteinase-1 inhibitors using several 3D QSAR techniques

Some three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) for a series of 84 proline-based plus 12 structurally more diversified nonproline matrix metalloproteinase inhibitors. The structures of these inhibitors were built from a structure template extracted from the crystal structure of stromelysin. The structures built were divided into the training and test sets for both the CoMFA and CoMSIA analyses for each being composed of 60 and 24 inhibitors, respectively. The structures in the training set were aligned using some alignment rules derived from the analysis of the Ligplot program on a recent crystal structure of ligand-collagenase-1 complex. Some stepwise CoMSIA's were performed on the aligned training set on which the best CoMFA result was obtained. The best CoMSIA model was identified from the stepwise results, and the corresponding pharmacophore features were used for the construction of a pharmacophore hypothesis by the Catalyst 4.9 program. The training set was extended to include 11 structurally more diversified and nonproline inhibitors. To construct a pharmacophore hypothesis, the conformation of 60 structurally aligned proline-based inhibitors was fixed, while that of the 11 structurally more diversified nonproline inhibitors was allowed to vary during the hypothesis construction process. It was found that the predicted activities by the top hypothesis constructed for both the training and test sets were as good in statistics as those predicted by the best CoMSIA model from which the hypothesis was derived. The top hypothesis was mapped onto the structures of several highly active inhibitors selected from both the training and test sets. The goodness of mapping on each inhibitor was found to be correlated well with the activity of each inhibitor.     

2,4-二氨基嘧啶类FAK抑制剂的3D-QSAR研究

摘要 本文选取42个2,4-二氨基嘧啶类FAK小分子抑制剂,分别以比较分子场分析法（CoMFA）与相似性指数分析法（CoMSIA）构建3D-QSAR模型,评价模板分子、公共骨架点、最小能量优化参数、分子构象等因素对模型优化的影响。分析最优模型中立体场、静电场以及氢键等因素对抑制剂活性的影响,并应用分子对接分析该类抑制剂与FAK蛋白的相互作用。结果表明选择16号化合物作为模板分子,骨架A作为公共骨架点,最小能量优化参数中电荷、最大迭代系数、最低能量限定值分别为MMFF94、1000、0.01 Kcal/mol时所构建的模型最优。以CoMFA和CoMSIA构建的3D-QSAR模型的交叉验证系数（q2）分别为0.666和0.736,非交叉验证系数（R2）分别为0.990和0.989,表明此模型具有良好的预测能力。分子对接分析显示,其与FAK的氨基酸残基CYS502、ASP564形成了重要的氢键作用,并与周围残基形成了较强的疏水作用。通过3D-QSAR的构建与分子对接分析,可指导2,4-二氨基嘧啶类FAK小分子抑制剂的进一步结构优化设计。