苯并呋喃类N-肉豆蔻酰基转移酶抑制剂的三维定量构效关系研究

摘要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), 系统地研究了40个苯并呋喃类N-肉豆蔻酰基转移酶(NMT)抑制剂的三维定量构效关系. 在CoMFA研究中, 考察了网格点步长对模型统计结果的影响. 在CoMSIA研究中, 研究了各种分子场组合、 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可得到最佳模型. 所建立的CoMFA和CoMSIA模型的交叉相关系数q2值分别为0.759和0.730, 均具有较强的预测能力. 利用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并呋喃环上各位置取代基对抑酶活性的影响, 为进一步结构优化提供了重要依据.     

酪氨酸蛋白磷酸酯酶1B抑制剂的分子对接和三维定量构效关系研究

用一种柔性分子对接方法(FlexX)将12个2-草酰胺苯甲酸类抑制剂和酪氨酸蛋白磷酸酯酶(PTP1B)活性口袋进行分子对接,对接程序预测的抑制剂和酶之间的相互作用能与抑制活性之间有很好的相关性(非线性相关系数R²达0.859),这说明对接结果可以比较准确地预测抑制剂和PTP1B之间的结合模式.然后,将33个同类抑制剂的骨架叠合在分子对接预测的结合构象上,用比较分子力场分析方法(CoMFA)对其进行三维定量活性构效关系研究,得到的CoMFA模型具有很好的统计相关性(交互验证回归系数q²为0.650),并可以准确地预测测试集6个化合物的活性(平均标准偏差为0.177).同时,由CoMFA模型得出的抑制剂改造信息与用FlexX预测的结合模式是一致的,进一步证明我们预测的结合模式是正确的.为研究这类抑制剂和PTP1B的结合模式及对抑制剂进行结构改造提供了信息.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式. 首先, 用分子对接确定抑制剂与GSK-3β结合模式及其相互作用; 然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析. 两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA), 证明该模型具有很好的统计相关性, 同时也说明该模型具有较高的预测能力.根据该模型提供的信息, 设计出9个预测活性较好的分子.     

家蝇与大鼠GABA受体抑制剂的药效团模型及其3D-QSAR研究

采用DISCOtech方法,用7个大鼠γ-氨基丁酸(GABA)A受体抑制剂和11个家蝇GABAA受体抑制剂分别建立了其药效团模型;用CoMFA方法建立了22个大鼠GABAA受体抑制剂和29个家蝇GABAA受体抑制剂的3D-QSAR模型,模型的交叉验证相关系数分别为0.526和0.679,验证了药效团模型的合理性,为设计更高活性和更高选择性的化合物提供了参考     

A CoMFA analysis with conformational propensity: An attempt to analyze the SAR of a set of molecules with different conformational flexibility using a 3D-QSAR method

CoMFA analysis, a widely used 3D-QSAR method, has limitations to handle a set of SAR data containing diverse conformational flexibility since it does not explicitly include the conformational entropic effects into the analysis. Here, we present an attempt to incorporate the conformational entropy effects of a molecule into a 3D-QSAR analysis. Our attempt is based on the assumption that the conformational entropic loss of a ligand upon making a ligand-receptor complex is small if the ligand in an unbound state has a conformational propensity to adopt an active conformation in a complex state. For a QSAR analysis, this assumption was interpreted as follows: a potent ligand should have a higher conformational propensity to adopt an `active-conformation'-like structure in an unbound state than an inactive one. The conformational propensity value was defined as the populational ratio, N<sub>active</sub>/N<sub>stable</sub>, of the number of energetically stable conformers, N<sub>stable</sub>, to the number of `active-conformation'-like structures, N_active. The latter number was calculated by counting the number of conformers that satisfied the structural parameters deduced from the active conformation. A set of SAR data of imidazoleglycerol phosphate dehydratase inhibitors containing 20 molecules with different conformational flexibility was used as a training set for developing a 3D structure-activity relationship by a CoMFA analysis with the conformational propensity value. This resulted in a cross-validated squared correlation coefficient of the CoMFA model with the conformational propensity value (R ² _cross = 0.640) higher than that of the standard CoMFA model (R ² _cross = 0.431). Then we evaluated the quality of the CoMFA models by predicting the inhibitory activity for a new molecule.     

The novel 4-hydroxyphenylpyruvate dioxygenase inhibitors in vivo and in silico approach: 3D-QSAR analysis,molecular docking,bioassay and molecular dynamics

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only an important target enzyme for the treatment of type I tyrosinemia, but also a new target for design bleaching herbicides, and it plays key role in the biosynthesis of tocopherol and plastoquinone. Thirty-six known active pyridine derivatives were collected, and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models based on common skeleton were constructed to obtain novel HPPD herbicides with higher activity. Two new HPPD inhibitors were rationally designed and synthesized according to the CoMFA and CoMSIA models and verified by enzyme activity, biological assays, and molecular docking. The promising compound W1 ((E)-5-(3-(4-bromophenyl)acryloyl)-6-hydroxy-2,3-dihydropyridin-4(1H)-one) showed better AtHPPD inhibitory activity, and the bioassay results revealed that some weeds showed bleaching symptoms. The good binding stability of W1 and protein was confirmed by molecular dynamics simulation in 100 ns. These results would be highly useful in the progress of new HPPD inhibitors discovery.     

HIV-1逆转录酶抑制剂的3D-QSAR研究及分子设计

采用Topomer Co MFA方法对24个二芳基苯胺衍生物进行三维定量构效关系研究,建立了3DQSAR模型,所得优化模型的非交叉相关系数、交互验证系数以及外部验证的复相关系数分别为0.928,0.654和0.940,结果表明该模型具有良好的稳定性和预测能力。采用分子对接技术对药物与受体的作用机制进行了研究,结果显示,药物与HIV-1逆转录酶的LYS172,GLU138,LYS101等位点作用明显。运用这些信息进行分子设计,在理论上获得了一些具有较高活性的新的二芳基苯胺类抗艾滋病药物,该QSAR的研究结果可为新药合成提供理论参考。     

STAT3环烷烃并噻吩类衍生物抑制剂的三维定量构效关系研究

本文对STAT3抑制剂的化学结构与生物活性之间的关系进行研究。采用三维定量构效关系(3D-QSAR)中的比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法针对52个STAT3抑制剂建立3D-QSAR模型,阐明了抑制剂化学结构与其生物活性之间的关系。所构建的CoMFA模型交叉验证系数为0.548,非交叉验证系数为0.754,标准偏差为0.278,显著系数为58.297;所构建的CoMSIA模型交叉验证系数为0.892,非交叉验证系数为0.597,标准偏差为0.192,显著系数为57.794。结果显示CoMFA和CoMSIA模型具有良好的稳定性和预测能力。3D-QSAR模型等势图提供的相关场信息对新型STAT3抑制剂的设计具有指导意义。     

Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors

Three-dimension quantitative structure activity relationship (3D-QSAR) was one of the major statistical techniques to investigate the correlation of biological activity with structural properties of candidate molecules, and the accuracy of statistic greatly depended on molecular alignment methodology. Exhaustive conformational search and successful conformational superposition could extremely improve the predictive accuracy of QSAR modeling. In this work, we proposed a solution to optimize QSAR prediction by multiple-conformational alignment methods, with a set of 40 flexible PTP1B inhibitors as case study. Three different molecular alignment methods were used for the development of 3D-QSAR models listed as following: (1) docking-based alignment (DBA); (2) pharmacophore-based alignment (PBA) and (3) co-crystallized conformer-based alignment (CCBA). Among these three alignments, it was indicated that the CCBA was the best and the fastest strategy in 3D-QSAR development, with the square correlation coefficient (r²) and cross-validated squared correlation coefficient (q²) of comparative molecular field analysis (CoMFA) were 0.992 and 0.694; the r² and q² of comparative molecular similarity indices analysis (CoMSIA) were 0.972 and 0.603, respectively. The alignment methodologies used here not only generated a robust QSAR model with useful molecular field contour maps for designing novel PTP1B inhibitors, but also provided a solution for constructing accurate 3D-QSAR model for various disease targets. Undoubtedly, such attempt in QSAR analysis would greatly help us to understand essential structural features of inhibitors required by its target, and so as to discover more promising chemical derivatives.     

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...     

Molecular modelling studies on cinnoline-based BTK inhibitors using docking and structure-based 3D-QSAR

ABSTRACT

BTK inhibitors have been proved as an effective target for B-cell malignancies. Ibrutinib is the most advanced irreversible BTK inhibitor for treating mantle cell lymphoma/chronic lymphocytic leukaemia but with existing drug resistance and adverse effects. To design novel effective and safety reversible BTK inhibitors, 115 newly cinnoline analogues were selected to perform molecular docking and 3D-QSAR study because of the main scaffold similarity to Ibrutinib. Both established CoMFA and CoMSIA models obtained high predictive and satisfactory value. CoMFA/CoMSIA contour maps demonstrated that bulky substitutions are preferred at R₁ and R₃ positions, and introducing hydrophilic and negative electrostatic substitutions at R₁ positions is important for improving BTK inhibitory activities. These results will be useful to provide clues for rationally designing novel and high potency BTK inhibitors.     

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.     

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.     

Design Two Novel Tetrahydroquinoline Derivatives against Anticancer Target LSD1 with 3D-QSAR Model and Molecular Simulation

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, Rpred2 = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, Rpred2 = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.     

Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis

A novel procedure is proposed for 3D-QSAR analysis. The composition of 16 published QSAR datasets has been examined using Quantitative Series Enrichment Analysis (QSEA). The procedure is based on topomer technologies. A heatmap display in combination with topomer CoMFA and a novel series trajectory analysis revealed critical information for the assembly of structures into meaningful series. Global and local centroid structures can be determined from a similarity distance matrix and build the origins for stepwise model building by increasing the similarity radius around the centroid nucleus. The results indicate that the new procedure allows determination of whether compounds belong to an emerging structure-activity relationship and which compounds can be predicted within reliable limits.     

靛玉红类CDK1抑制剂的同源模建、分子对接及3D-QSAR研究

细胞周期蛋白依赖性激酶1的异常表达会导致G2期的停滞及多种肿瘤的发生,故CDK1近年来已成为一个理想的治疗靶点. 本文以细胞分裂调控蛋白2的同源体为模板,同源模建了CDK1的结构,并与靛玉红类小分子抑制剂进行分子对接. 分别运用三种叠合方法进行分子叠合,并在此基础上采用Sybyl 7.1中的比较分子场分析(CoMFA)模块及Discovery Studio 3.0中的三维定量构效关系(3D-QSAR)模块(以下简称为DS)分别建立了3D-QSAR模型. 其中,将分子对接叠合与公共骨架叠合联合运用的叠合方法所得3D-QSAR模型的评价参数是最佳的(CoMFA：q²=0.681,r²=0.909,r_pred.²=0.836; DS：q²=0.579,r²=0.971,r_pred.²=0.795,其中q²为交叉验证系数,r²为非交叉验证系数). 本文的研究结果在对靛玉红类小分子进行结构修饰设计出新的CDK1抑制剂方面,可提供重要的理论基础.     

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。     

CoMFA 3D-QSAR Analysis of Epothilones Based on Docking Conformation and Alignment

Epothilones belong to a class of novel microtubule stabilizing and anti-mitotic agents, which have a paclitaxel-like mechanism of action. A three-dimensional quantitative structure-activity relationship （3D-QSAR） model was built for epothilones by the method of comparative molecular field analysis （CoMFA） combined with the flexible docking technology. The docking CoMFA model gave a good cross-validated value of q2=0.784 with an optimized component of 6 and the conventional correlation coefficient of r^2=0.985. The statistical results show that the model has good ability to predict the activity of the studied compounds. At last, the docking CoMFA model was analyzed through contour maps complemented with MOLCAD-generated active site potential surface in the α,β-tubulin receptor, which can provide important information for the structure-based drug design.     

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.