噻吩并嘧啶酮类PDE7/PDE4双重抑制剂的特征结构北大核心CSCD

磷酸二酯酶7和4(phosphodiesterase 7 and 4,PDE7 and PDE4)作为特异性水解第二信使3',5'-环腺苷酸的蛋白酶,是治疗炎症等相关疾病的重要靶点。本文以37个噻吩并嘧啶酮类PDE7和PDE4双重抑制剂为研究对象,采用比较分子相似性指数分析(Co MSIA),研究其影响化合物抑制活性的特征结构信息。结果表明,这两类抑制剂的Co MSIA的预测能力较强(Rpre2≥0.80)。其影响分子生物活性的共同特征结构主要是:(1)噻吩环上的R_2取代基为疏水场的敏感区域;(2)嘧啶酮环和R_3取代基的链接基益于采用含氢键供体的亲水性基团;(3)噻吩环所在区域益于引入包含氢键供体的基团。研究还发现,PDE7抑制剂的R_1和R_2取代基,分别适宜结合小体积的亲水性基团和大体积的基团。PDE4抑制剂的嘧啶酮环和R3取代基的链接基益于结合正电基团。本研究所得的模型和信息,可为后续新型抑制剂的设计开发提供理论指导。     

噻吩并嘧啶酮类PDE7/PDE4双重抑制剂的特征结构

磷酸二酯酶7和4(phosphodiesterase 7 and 4,PDE7 and PDE4)作为特异性水解第二信使3',5'-环腺苷酸的蛋白酶,是治疗炎症等相关疾病的重要靶点。本文以37个噻吩并嘧啶酮类PDE7和PDE4双重抑制剂为研究对象,采用比较分子相似性指数分析(Co MSIA),研究其影响化合物抑制活性的特征结构信息。结果表明,这两类抑制剂的Co MSIA的预测能力较强(Rpre2≥0.80)。其影响分子生物活性的共同特征结构主要是:(1)噻吩环上的R_2取代基为疏水场的敏感区域;(2)嘧啶酮环和R_3取代基的链接基益于采用含氢键供体的亲水性基团;(3)噻吩环所在区域益于引入包含氢键供体的基团。研究还发现,PDE7抑制剂的R_1和R_2取代基,分别适宜结合小体积的亲水性基团和大体积的基团。PDE4抑制剂的嘧啶酮环和R3取代基的链接基益于结合正电基团。本研究所得的模型和信息,可为后续新型抑制剂的设计开发提供理论指导。     

花青素主要成分与HER-2激酶区的分子对接

用分子对接方法预测天然植物化学物质与受体蛋白的相互作用位点并探究作用机制。利用MVD(Molecular Virtual Docker 5.5)软件,以HER-2激酶区为受体模板建立活性位点,与12种花青素成分进行分子对接。结果表明12种化合物均能在同一活性腔中与HER-2激酶区对接(MolDock Score:苷元–105 kJ/mol,单葡糖苷–130 kJ/mol),主要作用力是疏水作用和氢键;该活性腔也是ATP与HER-2激酶区的结合(MolDock Score=–161 kJ/mol)位点,花青素的结合可能会干扰ATP与HER-2之间氢键的形成。提示花青素可能以竞争性结合方式阻碍ATP与HER-2的结合,抑制HER-2磷酸化激活及下游信号通路的激活,从而发挥抑癌活性。     

黄酮类醛糖还原酶抑制剂的三维定量构效关系研究

目的：建立黄酮类化合物抑制剂活性的三维定量构效关系模型,为进一步进行黄酮类醛糖还原酶抑制剂（ARI）的活性与三维结构关系的研究提供重要依据。方法：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75个新型ARI的三维定量构效关系。结果：CoMFA和CoMSIA模型的交互验证相关系数q^2值分别为0.603和0．706、非交互验证相关系数r2值分别为0.956和0.900。结论：CoMFA和CoMSIA模型均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各位置取代基对黄酮类醛糖还原酶抑制剂活性的影响,为进一步结构优化提供了重要理论依据。     

螺环哌啶类似物作为DOR激动剂的特征结构研究

阿片受体激动剂与特定阿片受体亚型结合,常用来治疗与外伤、癌症或心脏病相关的严重疼痛,是十分有吸引力的药用物质．阿片受体有3种经典亚型(δ, κ, μ),均有与其对应的激动剂．δ阿片受体(DOR)激动剂因其还有明显的抗焦虑、抗抑郁和器官保护作用,是非常有前景的药物．本文研究了一批共102个N-取代螺环哌啶类似物作为δ阿片受体激动剂的分子,采用比较分子力场(CoMFA)和比较分子相似性指数(CoMSIA)两种分析方法对所有分子进行了三维定量构效关系(3D-QSAR)研究,其中基于疏水场和氢键供体场参数建立的CoMSIA模型最佳,其模型结果为：Q²=0.501,R²_ncv=0.787,R²_pre=0.780,证明模型自我吻合良好,同时有较强的内部及外部预测能力．而模型的等势线图分析表明,在R₁处引入疏水性的取代基及在R₂处引入亲水性的取代基或氢键供体基团对提高激动剂活性有利．这些结论有助于更好地理解N-取代螺环哌啶类似物作为DOR激动剂的机理,为新型的δ阿片受体激动剂的设计和优化提供一定的指导．     

鸡血藤中黄酮类化合物与环氧合酶-2对接研究北大核心CSCD

探索黄酮类化合物抗环氧合酶-2的分子机理,筛选鸡血藤中选择性抗环氧合酶-2的黄酮类化合物。本研究应用Autodock 4.2软件对环氧合酶和环氧合酶抑制剂进行分子对接研究,建立阳性抑制剂结合自由能与抑制活性关系模型,并筛选鸡血藤中选择性抗环氧合酶-2的黄酮类化合物。阳性抑制剂与环氧合酶的对接模型R2分别为0.96997和0.84171,建立了预测能力较好的对接模型,可用于指导环氧合酶抑制剂的筛选。筛选结果表明,3',4',7-三羟基黄酮、儿茶素、没食子儿茶素、表儿茶素具有较强的环氧合酶-2选择性抑制活性,可作为母体用于新型抗炎药物设计。     

鸡血藤中黄酮类化合物与环氧合酶-2对接研究

探索黄酮类化合物抗环氧合酶-2的分子机理,筛选鸡血藤中选择性抗环氧合酶-2的黄酮类化合物。本研究应用Autodock 4.2软件对环氧合酶和环氧合酶抑制剂进行分子对接研究,建立阳性抑制剂结合自由能与抑制活性关系模型,并筛选鸡血藤中选择性抗环氧合酶-2的黄酮类化合物。阳性抑制剂与环氧合酶的对接模型R2分别为0.96997和0.84171,建立了预测能力较好的对接模型,可用于指导环氧合酶抑制剂的筛选。筛选结果表明,3',4',7-三羟基黄酮、儿茶素、没食子儿茶素、表儿茶素具有较强的环氧合酶-2选择性抑制活性,可作为母体用于新型抗炎药物设计。     

黄酮化合物色谱保留时间与其三维结构的关系研究

利用比较分子相似性指数分析（CoMSIA）方法,结合黄酮类化合物含有较多羟基、易形成较强分子内氢键的特点,建立了黄酮类化合物色谱保留时间与其三维结构的关系模型,以探讨黄酮类化合物色谱保留时间预测的新方法。模型交叉验证相关系数q2值为0．705,非交叉验证相关系数r2为0．981,表明模型具有较好的预测能力。该研究结果对进一步开展黄酮类化合物液相色谱保留参数与三维结构关系的研究提供了思路和方法。     

野葛葡糖基转移酶PlUGTs的同源建模及其活性位点分析

本文对PIUGTs进行同源建模,并分析其与底物结合的构象及活性位点。通过SWISS-MODEL在线对P1UGTs进行模板预测和选择,运用Swiss—PdbViewer软件显示和优化,利用ACDLABS绘制糖基供体小分子（酶结合底物）,最后通过AutoDock_ADT进行分子对接,并分析PIUGTs酶与不同底物结合的整体构象及分析活性位点。研究结果表明PIUGT1、PIUGT2及PIUGT3均能得到较好的三级构象,并且PIUGT1、PIUGT2与三种底物均可进行较好对接,H18,R278,N359为PIUGTI与三种对接构象活性中心所共有的氨基残基;而G16,H17,V19,T148,N370,E374,E390为PIUGT2与三种对接构象活性中心所共有的氨基残基,但PIUGT3未能得到较好的对接构象。由此推测PIUGT1和PIUGT2均能合成葛根素．而PIUGT3不能催化葛根素的合成。     

三种七叶一枝花活性成分抗新型冠状病毒的分子对接预测

以分子对接法探索中草药七叶一枝花治疗新型冠状病毒SARS-CoV-2的活性化合物。SARS-CoV-2主要通过其S蛋白与人体细胞表面的Angiotensin-converting enzyme 2(ACE2)受体结合。本研究通过分子对接模拟预测了七叶一枝花中富含的3种重楼皂苷(Ⅰ、Ⅵ、Ⅶ)与ACE2的结合亲和力。结果表明:三种重楼皂苷均能够与ACE2结合,结合自由能均低于-8 kcal/mol。三种化合物共同结合的氨基酸残基包括:Pro-346、Thr-347、Ala-348、Asp-350、Asn-394、His-401、Glu-402。三种药物结合以上位点的结构主要是共同的母核结构起着关键作用。另外,重楼皂苷Ⅰ与ACE2结合所需能量最低,而重楼皂苷Ⅵ与靶蛋白作用的关键氨基酸数量最多及形成的氢键数量最多。因此,基于这三种有效成分进行结构设计有望获得高效的SARS-CoV-2抑制剂,以期为COVID-19治疗药物发现提供研究基础。     

Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

The 3D quantitative structure-activity relationships of 31 quinoline nuclei containing compounds and their biological activity have been investigated to establish various models. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies resulted in reliable and significant computational models. The obtained CoMFA model showed high predictive ability with q(2) = 0.592, r(2) = 0.966 and standard error of estimation (SEE) = 0.167, explaining majority of the variance in the data with two principal components. Predictions obtained with CoMSIA steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor fields (q(2) = 0.533, r(2) = 0.985) showed high prediction ability with minimum SEE (0.111) and four principal components. The information obtained from the CoMFA and CoMSIA contour maps can be utilized for the design and development of topoisomerase-II inhibitors for synthesis.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

Three-dimensional quantitative structure-activity relationship (CoMFA and CoMSIA) studies on galardin derivatives as gelatinase A (matrix metalloproteinase 2) inhibitors

Three-dimensional quantitative structure-activity relationship models have been derived using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and molecule docking for the training sets of galardin-based matrix metalloproteinase inhibitors (MMPIs). The statistical values for the best models are significant. The models showed that the steric effect near the S1' pocket and hydrogen-bonding effect of the zinc binding group play key roles on the inhibitory activity of gelatinase A. The sets of the training and test proved the models were stable and predictive, and may have a good prediction for the inhibition activities of galardin derivatives as gelatinase A inhibitors. The results not only lead to a better understanding of the molecular mechanisms and structural requirements of gelatinase A inhibitors but also can help to design novel inhibitors against gelatinase A.     

Pharmacophore modeling, virtual screening and 3D-QSAR studies of 5-tetrahydroquinolinylidine aminoguanidine derivatives as sodium hydrogen exchanger inhibitors

Sodium hydrogen exchanger (SHE) inhibitor is one of the most important targets in treatment of myocardial ischemia. In the course of our research into new types of non-acylguanidine, SHE inhibitory activities of 5-tetrahydroquinolinylidine aminoguanidine derivatives were used to build pharmacophore and 3D-QSAR models. Genetic Algorithm Similarity Program (GASP) was used to derive a 3D pharmacophore model which was used in effective alignment of data set. Eight molecules were selected on the basis of structure diversity to build 10 different pharmacophore models. Model 1 was considered as the best model as it has highest fitness score compared to other nine models. The obtained model contained two acceptor sites, two donor atoms and one hydrophobic region. Pharmacophore modeling was followed by substructure searching and virtual screening. The best CoMFA model, representing steric and electrostatic fields, obtained for 30 training set molecules was statistically significant with cross-validated coefficient (q(2)) of 0.673 and conventional coefficient (r(2)) of 0.988. In addition to steric and electrostatic fields observed in CoMFA, CoMSIA also represents hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. CoMSIA model was also significant with cross-validated coefficient (q(2)) and conventional coefficient (r(2)) of 0.636 and 0.986, respectively. Both models were validated by an external test set of eight compounds and gave satisfactory prediction (r(pred)(2)) of 0.772 and 0.701 for CoMFA and CoMSIA models, respectively. This pharmacophore based 3D-QSAR approach provides significant insights that can be used to design novel, potent and selective SHE inhibitors.     

3D-QSAR studies on malonyl coenzyme A decarboxylase inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of Malonyl Co-A decarboxylase (MCD) inhibitors (Cheng et al. J. Med. Chem.2006, 49, 1517-1525 and Cheng et al. Bioorg. Med. Chem. Lett.2006, 16, 695-700). These inhibitors have shown protective action on the ischemic heart by inhibiting fatty acid oxidation. The CoMFA model produced statistically significant results, with the cross-validated and conventional correlation coefficients being 0.544 and 0.986, respectively. The best results were obtained by combining steric, electrostatic, hydrophobic, and H-bond acceptor fields in CoMSIA, in which case the respective cross-validated and conventional correlation coefficients were 0.551 and 0.918. The predictive ability of CoMFA and CoMSIA, determined using a test set of 24 compounds, gave predictive correlation coefficients of 0.718 and 0.725, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps may be of utility in the design of more potent MCD inhibitors.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

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 (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

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 (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Elucidating inhibitory models of the inhibitors of epidermal growth factor receptor by docking and 3D-QSAR

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are attractive targets for anti-tumor drug design. Although thousands of their ligands have been studied as potential inhibitors against PTKs, there is no QSAR study that covers different kinds of inhibitors with observable structural diversity. However, by using this approach, we could mine far more useful information. Hence in order to better understand the binding model and the relationship between the physicochemical properties and the inhibitory activities of different kind of various inhibitors, molecular docking and 3D-QSAR, viz. CoMFA and CoMSIA, were combined to study 124 reported inhibitors with different scaffolds. Based on the docked binding conformations, highly reliable and predictive 3D-QSAR models were derived, which reveal how steric, electrostatic, and hydrophobic interactions contribute to inhibitors' bioactivities. This result also demonstrates that it is possible to include different kinds of inhibitors with observable structural diversity into one 3D-QSAR study. Therefore, this study not only casts light on binding mechanism between EGFR and its inhibitors, but also provides new hints for de novo design of new EGFR inhibitors with observable structural diversity.