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。     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.     

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。     

R基团搜索技术用于PTH类Tau蛋白抑制剂的分子设计

对26个PTH类Tau蛋白抑制剂进行了Topomer CoMFA研究, 建立了拟合及预测能力良好的Topomer CoMFA模型, 获得的模型拟合、 交互验证及外部预测的复相关系数分别为0.976, 0.603和0.795, 估计标准偏差和Fisher验证值F分别为0.110和115.778. 使用ZINC化合物数据集作为结构片段源, 通过三维定量构效关系(3D-QSAR)模型搜索具有特定活性贡献的R基团. 以样本中活性最高的1号分子过滤, R₁和R₂贡献值均提高了20%的片段分别有9个与2个. 以此交替取代1号样本的R₁与R₂, 得到18个新颖化合物并预测其活性, 其中的15个预测活性值优于模板分子. 研究结果表明, Topomer search可有效地用于分子设计, 所设计的分子为阿尔茨海默病(AD)药物的研发提供了新的候选物.     

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.     

6-氮杂吲唑类Pim-1激酶抑制剂的3D-QSAR、药效团模型研究和分子设计

Pim-1 激酶通过作用于多种信号通路或靶点影响肿瘤的发生发展,近年来被认为是肿瘤治疗的良好靶标。本文采用SYBYL-X2. 1. 1软件中的TopomerCoMFA、GALAHAD模块建立计算机模型,研究39个基于6-氮杂吲唑环的Pim-1激酶抑制剂的三维定量构效关系及药效团特征元素。结果显示,TopomerCoMFA建模所得交叉验证系数（q2）和相关系数（r2）分别为0. 756和0. 951,结合外部验证表明此3D-QSAR模型具有较高预测能力及较好的统计学稳定性,同时,用等势图描述了R1、R2基团处立体场、静电场对活性的具体影响。药效团研究结果表明,含氢键受体的芳香杂环母核结构,以及侧链取代基中含有芳香杂环结构对化合物的活性贡献较大。最后根据上述模型信息新设计了15个Pim-1激酶抑制剂分子并完成活性预测及分子对接模式研究,其中4个分子的预测pIC50高于建模分子中活性最好的化合物17,Surflex-Dock分析显示新设计分子均与Pim-1激酶形成较强氢键相互作用。基于6-氮杂吲唑环的Pim-1激酶抑制剂的3D-QSAR模型以及药效团模型可用于指导新型抑制剂的结构优化,为设计和开发具有较高活性的新型Pim-1激酶抑制剂提供有效帮助。     

Molecular Modeling and Design of Arylthioindole Derivatives as Tubulin Inhibitors

Three-dimensional quantitative structure activity relationship （3D-QSAR） and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carried out. An optimal 3D-QSAR model from the comparative molecular field analysis （CoMFA） for training set with significant statistical quality （R2=0.898） and predictive ability （q2=0.654） was established. The same model was further applied to predict pIC50 values of the compounds in test set, and the resulting predictive correlation coefficient R2（pred） reaches 0.816, further showing that this CoMFA model has high predictive ability. Moreover, the appropriate binding orientations and conformations of these compounds interacting with tubulin are located by docking study, and it is very interesting to find the consistency between the CoMFA field distribution and the 3D topology structure of active site of tubulin. Based on CoMFA along with docking results, some important factors improving the activities of these compounds were discussed in detail and were summarized as follows： the substituents R3-R5 （on the phenyl ring） with higher electronegativity, the substituent R6 with higher eleetropositivity and bigger bulk, the substituent R7 with smaller bulk, and so on. In addition, five new compounds with higher activities have been designed. Such results can offer useful theoretical references for experimental works.     

二肽肽酶IV抑制剂的三维定量构效关系研究

二肽肽酶IV是一类用于治疗II型糖尿病具有潜在价值的关键酶, 很多此类酶的抑制剂用于处理此病具有相当好的有效性. 一系列N-取代的甘氨酰氰基吡咯烷衍生物对于二肽肽酶具有高的活性和选择性. 我们使用比较分子力场分析方法建立DPP-IV抑制剂——N-取代的甘氨酰氰基吡咯衍生物的三维定量构效关系, 该模型为设计用于治疗II型糖尿病的高效DPP-IV抑制剂提供结构信息. CoMFA模型的交叉验证相关系数q²＝0.575, 非交叉验证相关系数r²＝0.981, 绝对误差S＝0.184, F_9.68＝388.5. 使用七个预测集检验了模型的预测能力. 所得的模型解释了已有的构效关系, 并对同类化合物有较好的预测能力, 该模型可用于指导新型的DPP-IV抑制剂的设计与优化.     

3 D-QSAR Analysis of Agonists of nAChRs： Epibatidine Analogues

A 3 D-QSAR about nAChRs agonists epibatidine analogues was performed using theCoMFA and CoMSIA. The correlation coefficients were R2cv = 0.546, R2cv = 0.907 in CoMFA andR2cv = 0.655, R2,~ = 0.962 in CoMSIA of the final model. The prediction using the final models tothe test set was r2 = 0.675 in CoMFA and r2 = 0.462 in CoMSIA. This model will be useful in thedesign of novel compounds with high affinity.     

Homology model-guided 3D-QSAR studies of HIV-1 integrase inhibitors

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed on the basis of the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T ) and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir, and their analogs; our synthesized 3-keto salicylic acid IN inhibitor series; as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II, and docking-based alignment, III, were used to develop 3D-QSAR models 1, 2, and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) model 3 performed better than the atom-fit (ligand-based) models 1 and 2, in terms of statistical significance and robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, model 4. Model 4 afforded good q2 values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r2 values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.     

CoMFA Model and Molecular Design of Anti-excitatory Activity for Benzodiazepinooxazole Derivatives against Mice

A 3D-QSAR study was conducted to analyze the anti-excitatory activity(p E) of benzodiazepinooxazole derivatives to mice by the comparative molecular field analysis(CoMFA) method. Among the 54 active molecules, a training set of 46 compounds was randomly selected to construct the CoMFA model; the remaining compounds, together with template molecule(No. 54) and two newly designed molecules constitute a test set of 17 compounds to validate the model. The obtained cross-validation coefficient(R_(cv)~2), the non-cross validation coefficient(R~2), and the test value F of the CoMFA model for training set are 0.516, 0.899, and 57.57,respectively. The model was used to predict the activities of all compounds in the training and testing sets, and the results indicated that the model had good correlation, strong stability and good predictability. Based on the 3D contour maps, eight novel benzodiazepinooxazole derivatives with higher anti-excitatory activity were designed.However, the effectiveness of these novel benzodiazepinooxazole derivatives is still needed to be verified by the experimental results.     

Comparative Molecular Field Analysis （CoMFA） of Curcumin-related Compounds for Anticancer Activity

Three-dimensional （3D） quantitative structure-activity relationship （QSAR） studies of 44 curcumin-related compounds have been carried out based on our previously reported result for their anticancer activity against pancreas cancer Panc-I cells and colon cancer HT-29 cells. The established 3D-QSAR models from the comparative molecular field analysis （CoMFA） in training set showed not only significant statistical quality, but also satisfying predictive ability, with high correlation coefficient values （R12= 0.911, R22= 0.985） and cross-validation coefficient values （q2= 0.580, q22= 0.722）. Based on the CoMFA contour maps, some key structural factors responsible for anticancer activity of these series of compounds were revealed. The results provide some useful theoretical references for understanding the mechanism of action, designing new curcumin-related compounds with anticancer activity and predicting their activities prior to synthesis.     

Topomer CoMFA,HQSAR Studies and Molecular Docking of 2,5-Diketopiperazine Derivatives as Oxytocin Inhibitors

Topomer comparative molecular field analysis(Topomer Co MFA) and holographic quantitative structure-activity relationship(HQSAR) for 130 2,5-diketopiperazine derivatives were used to build a three-dimensional quantitative structure-activity relationship(3D-QSAR) model. The results show that the models have high predictive ability. For Topomer CoMFA, the cross-validated q~2 value is 0.710 and the non-cross-validated r~2 value is 0.834. The most effective HQSAR model shows that the cross-validation q~2 value is 0.700, the non-cross-validated r~2 value is 0.815, and the best hologram length value is 353 using connections and bonds as fragment distinctions. 50 highly active 2,5-diketopiperazine derivatives were designed based on the three-dimensional equipotential map and HQSAR color code map. Finally, the molecular docking method was also used to study the interactions of these new molecules by docking the ligands into the diketopiperazine active site, which revealed the likely bioactive conformations. This study showed that there are extensive interactions between the new molecule and Arg156, Arg122 residues in the active site of diketopiperazine. These results provide useful insights for the design of potent of the new 2,5-diketopiperazine derivatives.     

CoMFA 3D-QSAR analysis of HIV-1 RT nonnucleoside inhibitors, TIBO derivatives based on docking conformation and alignment

HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as nonnucleoside RT inhibitors (NNRTIs) and nucleoside analogues. NNRTIs bind in a region not associated with the active site of the enzyme. Within the NNRTI category, there is a set of inhibitors commonly referred to as TIBO inhibitors. Fifty TIBO inhibitors were used in the work to build 3-D QSAR models. The two known crystal structures of complexes are used to investigate and validate the docking protocol. The results show that the docking simulations reproduce the crystal complexes very well with RMSDs of approximately 1 A and approximately 0.6 A for 1REV and 1COU, respectively. The alignment of molecules and "active" conformation selection are the key to a successful 3D-QSAR model by CoMFA. The flexible docking (Autodock3) was used on determination of "active" conformation and molecular alignment, and CoMFA and CoMSIA were used to develop 3D-QSAR models of 50 TIBOs in the work. The 3D-QSAR models demonstrate a good ability to predict the activity of studied compounds (r2 = 0.972, 0.944, q2 = 0.704, 0.776). It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the binding structure of the complex. The results demonstrate that the combination of ligand-based and receptor-based modeling is a powerful approach to build 3D-QSAR models.     

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.     

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.     

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.