R基团搜索技术用于HIV-1逆转录酶抑制剂的分子设计

本文采用基于R基团搜索技术的Topomer CoMFA方法对41个人类免疫缺陷病毒(HIV-1)逆转录酶抑制剂进行了三维定量构效关系(3D-QSAR)分析。所得优化模型的拟合、交互验证及外部验证的复相关系数分别为0.995、0.859和0.945。采用Topomer Search技术对ZINC数据库进行R基团的虚拟筛选,得到R贡献高的基团,以活性最高的13号分子为模板进行过滤得到1个Ra基团和20个Rb基团。并以此设计得到20个新化合物分子,其中有19个化合物的预测活性值高于13号分子。研究结果表明,所建立的Topomer CoMFA模型具有良好的稳定性和预测能力,基于R基团的Topomer Search技术可以有效筛选并设计出新的HIV-1逆转录酶抑制剂,为抗艾滋病新药设计提供了理论依据。     

分子对接技术用于马来酰亚胺类GSK-3α抑制剂的作用特征分析

糖原合酶激酶-3α(GSK-3α)是治疗阿尔兹海默症(AD)的关键靶点之一.采用基于R基团的搜索组合分子对接研究了GSK-3α抑制剂的作用特征.以45个马来酰亚胺类GSK-3α抑制剂分子为训练集,采用Topomer CoMFA建立3D-QSAR模型,其拟合与留一法交互验证的复相关系数和标准差分别为r2=0.797,SD=0.210,q2cv=0.611,SDcv=0.280,对22个测试集样本外部预测的复相关系数与标准差分别为r2pred=0.703,SDpred=0.213.以Topomer Search搜索技术设计了25个理论上具有更高活性的新型分子.分子对接对比研究表明,新设计的分子与建模样本同GSK-3α的作用位点具有类似的作用特征,且与对比文献一致.该研究为AD治疗的分子设计与研发提供了新的思路.     

R基团搜索技术用于硼酸三肽蛋白酶体抑制剂的分子设计

本文采用Topomer CoMFA对44个Tyropeptin硼酸三肽类蛋白酶体抑制剂进行三维定量构效关系(3D-QSAR)分析。所得最优模型的拟合、交互验证、及外部验证的复相关系数分别为0.983、0.651、0.963。采用Topomer search对ZINC数据库进行R基团的虚拟筛选,得到具有特定活性贡献的R基团,以活性最高的分子为模板过滤,得到7个R1和5个R2基团。并以此设计得到活性优于模板分子的20个新化合物。结果表明,所建立的Topomer CoMFA模型具有良好的稳定性和预测能力,基于R集团的Topomer search技术可以有效筛选,并为设计出新的蛋白酶体抑制剂提供理论依据。     

基于Topomer CoMFA和Surflex-dock的GSK-3β抑制剂的3D-QSAR与作用模式研究

GSK-3β的过度表达可导致人脑神经细胞内Tau蛋白的过磷酸化,从而介导阿尔兹海默病(Alzheimer’s disease,AD)的发生.本文旨在研究GSK-3β的马来酰胺类抑制剂的三维定量构效关系(3D-QSAR)及新抑制剂分子与GSK-3β的作用机制.采用基于R基团搜索技术的Topomer CoMFA建立了49个马来酰胺类GSK-3β抑制剂的3D-QSAR模型,并用包括25个样本的测试集验证模型的外部预测能力.所得优化模型的拟合、交互验证以及外部验证的复相关系数分别为0.928,0.790和0.725.采用Topomer search在ZINC分子数据库中进行虚拟搜索,设计了28个可能具有更高活性的新抑制剂.借助Surflex-dock分子对接研究了新抑制剂与GSK-3β作用模式与机制.结果显示,新抑制剂与GSK-3β的Asp133,Tyr134,Val135和Pro136等位点作用显著.     

类黄酮抑制 IP6Ks 的3D-QSAR 研究

采用基于R基团搜索技术的Topomer CoMFA建立了14个类黄酮类肌醇六磷酸激酶抑制剂的3D-QSAR模型,研究了类黄酮化合物对肌醇六磷酸激酶(IP6Ks)活性的抑制作用.该模型的主成分数为3,拟合与留一法交互验证的复相关系数以及F检验值分别为q~2=0.842,q_(st)~2=0.26;r~2=0.965,r_(st)~2=0.12;F=91.519.在此基础上通过Topomer Search进行分子片段筛选,对化合物8,14和6进行重新拼接设计,其预测活性可以分别提高12.76倍、9.27倍和62%.运用Surflex-dock分子对接法研究了实验数据中活性最高的化合物6和活性最低的化合物10与IP6Ks的PDB结构的作用机制,发现并验证了之前所建立的Topomer CoMFA模型构效关系分析研究的结果,进一步阐明了化合物6抑制活性更高的原因.结果表明,在类黄酮分子结构的C(5),C(7)和C(4′)位上,取代基团的大小和静电性质对其抑制活性产生重要的影响.本研究可能对以天然产物设计和合成具有更好生物活性的IP6Ks抑制剂具有指导作用.     

类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

采用基于R基团搜索技术的Topomer CoMFA建立了30个类黄酮类P糖蛋白抑制剂的三维定量构效关系(3D-QSAR)模型, 并用包括9个样本的测试集验证模型的外部预测能力. 所得模型的拟合、 交互验证以及外部验证的复相关系数分别为r²=0.971, q²=0.728和\begin{array}{c}{r}_{\mathit{pred}}^2\end{array}=0.816. 在此基础上, 运用Surflex-dock分子对接法研究了白杨素及其异戊烯化衍生物与P糖蛋白的作用模式. 结果表明, 异戊烯化修饰可显著提高类黄酮的亲脂性, 修饰产物能更好地与P糖蛋白的疏水性口袋契合, 二者结合程度高.     

含氟农药的比较分子场分析研究

用比较分子场分析(CoMFA)方法对112种含氟农药分子的生物活性及毒性同时进行了定量构效关系研究。用78个化合物作为训练集,以距离比较方法(DISCO)确认的药效团为叠合规则构建CoMFA模型,发现影响活性的立体场与静电场的贡献分别为60.4%和39.6%,影响毒性的立体场与静电场的贡献分别为59.2%和40.8%。药效模型与毒效模型在交叉验证时的相关系数平方(R^2)分别为0.652和0.611,非交叉验证的R^2分别为0.982和0.977,方差比F(8,69)值分别为463.6及362.9,活性和毒性的标准偏差-极差比s/△γ值分别为3.6%和2.9%,表明模型具有较好的自预测能力。对测试组34个化合物进行了活性和毒性的预测,活性与毒性预测的标准偏差-极差比s/△γ值分别为10.4%和6.4%。最后,还建立了一个由97个化合物构建的扩大的模型,各种统计量得到了进一步提高。并预计了一个活性较高且毒性很低的新化合物。     

取代嘧啶衍生物抗前列腺癌活性的3D-QSAR研究

运用比较分子力场分析(CoMFA)方法,建立18种取代嘧啶衍生物抗前列腺癌活性(pM)的三维定量构效关系。训练集中15个化合物用于建立预测模型,测试集13个化合物(含10号模板分子和新设计的9个分子)作为模型验证。建立的CoMFA模型的交叉验证系数(R_ev²)、非交叉验证系数(R²)分别为0.344、0.935,说明所建模型具有较强的鲁棒性和良好的预测能力。该模型中立体场、静电场贡献率依次为71.6%、28.6%。影响取代嘧啶衍生物抗前列腺癌活性的主要因素是取代基的疏水作用和空间位阻,其次是取代基的库仑力、氢键及配位作用。基于此研究结果,设计了9个新化合物,其抗前列腺癌活性有待医学实验验证。     

肿瘤血管阻断剂黄酮-8-乙酸类衍生物的三维定量关系研究

选择了肿瘤血管阻断剂黄酮-8-乙酸类衍生物, 采用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法进行三维定量构效关系的研究. 33个化合物建立了预测模型, 6个化合物作为训练集进行模型验证. 其中CoMFA模型的交叉验证系数q²＝0.621, 最佳主成分数为4, 标准偏差s_press＝0.345, 非交叉相关系数r²＝0.945, 标准偏差s＝0.131, F＝120.455. CoMSIA模型的交叉验证系数q²＝0.700, 最佳主成分数为5, 标准偏差s_press＝0.312, 非交叉相关系数r²＝0.946, 标准偏差s＝0.133, F＝94.193. 计算结果表明, 构建的CoMFA和CoMSIA模型具有良好的预测能力, 可用于指导该类化合物的设计.     

1,2-萘醌类化合物抑制PTP1B的三维定量构效关系研究

蛋白酪氨酸磷酸酶1B (protein tyrosine phosphatase 1B, PTP-1B)是近年来发现的治疗II型糖尿病的新靶点, 1,2-萘醌类化合物对PTP-1B有较好的抑制活性, 具有良好的药用前景. 为了设计出本类化合物抑制效果更好的分子构型, 用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对该类化合物进行了三维定量构效关系(3D-QSAR)的研究, 并建立了相关的预测模型. 其中, CoMFA模型的交叉验证相关系数(q²)为0.555, 非交叉验证相关系数(r²)为0.991, 标准偏差(SEE)为0.049, F值为564.910. CoMSIA模型的q²为0.558, r²为0.991, SEE为0.050, F值为542.773. 计算结果表明, 获得的CoMFA和CoMSIA模型具有良好的预测能力, 可以应用于指导该类化合物的设计.     

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship,virtual screening,and docking simulations

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r²), cross‐validation (q²), correlation coefficient of external validation (Q²_ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.     

A 3D-QSAR Study of HIV-1 Integrase Inhibitors Using RASMS and Topomer CoMFA

Acquired Immunodeficiency Syndrome(AIDS) is a significant human health threat around the world. Therefore, the study of anti-human immunodeficiency virus(HIV) drug design has become an important task for today's society. In this paper, a three-dimensional quantitative structure-activity relationships study(3 D-QSAR) was conducted on 53 HIV-1 integrase inhibitors(IN) using random sampling analysis on molecular surface(RASMS) and Topomer comparative molecular field analysis(Topomer CoMFA). The multiple correlation coefficients of fitting, cross-validation, and external validation of two models were 0.926, 0.815 and 0.908 and 0.930, 0.726 and 0.855, respectively. The results indicated that two models obtained had both favorable estimation stability and good prediction capability. Topomer Search was used to search appropriate R groups from ZINC database, and 28 new compounds were designed thereby. The Topomer CoMFA model was subsequently used to predict the biological activity of these compounds, showing that 24 of the new compounds were more active than the template molecule. Ligands of the template molecule and new designed compounds were used for molecular docking to study the interaction of these compounds with the protein receptor. The results show that the ligands would form hydrogen-bonding interactions with the residues LEU58, THR83, GLN62, MET155, LYS119 and ALA154 of the protein receptor generally, thereby providing additional insights for the design of even more effective HIV/AIDS drugs.     

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。     

QSAR and Pharmacophore Studies of Thiazolidine-4-carboxylic Acid Derivatives as Novel Influenza Neuraminidase Inhibitors Using HQSAR, Topomer CoMFA and CoMSIA

In order to understand the chemical-biological interactions governing their activities toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR (hologram quantitative structure activity relationship), Topomer CoMFA and CoMSIA (comparative molecular similarity indices analysis) models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients (R2) of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients (Q2) of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set (R2test) of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set (Q2ext) of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.     

含磷嘧啶类CDK9抑制剂的分子对接、3D-QSAR和分子动力学模拟

选取64个具有潜力的含磷嘧啶类细胞周期依赖性蛋白激酶(CDK9)小分子抑制剂,采用分子对接方法研究了该类小分子与CDK9的结合作用,结果表明,分子构象、氢键形成、疏水性和氨基酸残基Cys106在此类抑制剂与CDK9的结合过程中具有重要作用.在配体叠合的基础上,运用比较分子力场分析(Co MFA)、比较分子相似性指数分析(Co MSIA)和Topomer Co MFA(T-COMFA)研究了分子结构与抑制活性的关系,发现由训练集立体场、静电场和疏水场组合的Co MSIA模型为最优模型,其内部交叉验证相关系数(Q2=0.557)、非交叉验证相关系数(R2=0.959)和外部预测相关系数(r2=0.863)具有统计学意义,该模型的三维等值线图直观显示了化合物的活性与其三维结构的关系.根据这些结果设计了10个具有新结构的含磷嘧啶类化合物,分子对接和分子动力学模拟结果表明,新化合物和CDK9的结合模式与原化合物64相同,自由能分析从理论上证明了新化合物64d的CDK9抑制活性优于化合物64,并且显示含磷基团与残基Asp109的静电场能在化合物与CDK9作用过程中有重要作用.     

Molecular Docking and 3D QSAR Research of Indolocarbazole Series as Cyclin-Dependent Kinase Inhibitors

Fifty indolocarbazole series as cyclin-dependent kinase inhibitors (CDKs) are used to establish a threedimensional quantitative structure-activity relationship (3D QSAR) model based on docking conformations resulting from the Topomer comparative molecular field analysis (Topomer CoMFA). The statistic parameters show that the cross-validation (q²), the multiple correlation coefficient of fitting (r²), and external validation statistic (Q_ext²) are 0.953, 0.968, and 0.954, respectively. It is demonstrated that this Topomer CoMFA model has good stability and prediction ability. The methodology of the fragment-based drug design (FBDD) was also used to virtually screen new CDKs by the Topomer Search technology. Four similar substitutional groups selected from the ZINC database were added to the basic scaffold. As a result, 18 new CDKs with high activities were obtained. The template molecule and new designed compounds are used to study the binding relationship between the ligands and the receptor protein with Surflex-Dock. The docking results suggest good binding interactions of the designed compounds with protein. There are several hydrogen bondings between CDKs with amino acid residues of LYS33, LYS89, ASP86, LEU83, GLU81.     

3D-QSAR Analysis of Naphthyltriazole(Lesinurad) Analogs as Potent Inhibitors of Urate Transporter 1

To obtain useful information for identifying inhibitors of urate transporter 1(URAT1), three-dimensional quantitative structure-activity relationship(3 D-QSAR) analysis was conducted for a series of lesinurad analogs via Topomer comparative molecular field analysis(CoMFA). A 3 D-QSAR model was established using a training set of 51 compounds and externally validated with a test set of 17 compounds. The Topomer CoMFA model obtained(q^2 = 0.976, r2 = 0.990) was robust and satisfactory. Subsequently, seven compounds with significant URAT1 inhibitory activity were designed according to the contour maps produced by the Topomer CoMFA model.