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.     

QSAR and Molecular Docking Studies of Pyrimidine-Coumarin-Triazole Conjugates as Prospective Anti-Breast Cancer Agents

Cancer is a life-threatening disease and is the second leading cause of death worldwide. Although many drugs are available for the treatment of cancer, survival outcomes are very low. Hence, rapid development of newer anticancer agents is a prime focus of the medicinal chemistry community. Since the recent past, computational methods have been extensively employed for accelerating the drug discovery process. In view of this, in the present study we performed 2D-QSAR (Quantitative Structure-Activity Relationship) analysis of a series of compounds reported with potential anticancer activity against breast cancer cell line MCF7 using QSARINS software. The best four models exhibited a r² value of 0.99. From the generated QSAR equations, a series of pyrimidine-coumarin-triazole conjugates were designed and their MCF7 cell inhibitory activities were predicted using the QSAR equations. Furthermore, molecular docking studies were carried out for the designed compounds using AutoDock Vina against dihydrofolate reductase (DHFR), colchicine and vinblastine binding sites of tubulin, the key enzyme targets in breast cancer. The most active compounds identified through these computational studies will be useful for synthesizing and testing them as prospective novel anti-breast cancer agents.     

QSAR Studies on the Calanolide Analogues as Anti-HIV-1 Agents

The Quantitative Structure-Activity Relationship (QSAR) of a series of novel calanolide analogues,which exhibit inhibitory activities of HIV-1,has been studied with a combined method of ab initio (HF),molecular mechanics (MM+) and statistics. The established QSAR model (Eq. 1) shows a reasonable regressive performance (R2 = 0.885). Both the surface area of the substituted group attached on C10,SR3,and the distance between atoms O13 and X14 (O,N,S),L,of the calanolide analogues play important roles in determining the inhibitory activity of HIV-1.     

Molecular Docking and 3D-QSAR Studies on a Series of Fused Heterocyclic Amides as B-Raf Inhibitors

B-Raf has been identified as promising targets for novel anticancer agents. To further explore the interactions between small molecules and B-Raf, and to elucidate structural characteristics that influence the B-Raf kinase activity, molecular docking and three-dimensional quantitative structure-activity relationship(3D-QSAR) studies were performed on a dataset of 75 Type Ⅱ inhibitors. Molecular docking was applied to explore the detailed binding process between the inhibitors and B-Raf kinase in its DFG-out inactive conformation. Based on the conformations obtained by molecular docking strategy, 3D-QSAR models, including comparative molecular field analysis(CoMFA) and comparative molecular similarity indexes analysis(CoMSIA), were constructed. The established 3D-QSAR models show significant statistical quality and satisfactory predictive ability, with high q~2 and r~2 values: CoMFA model(q~2= 0.759, r~2 = 0.922), and CoMSIA model(q~2 = 0.685, r~2 = 0.945). The systemic external validation indicated that both CoMFA and CoMSIA models were quite robust and possess high predictive abilities with r~2 pred values of 0.633 and 0.708, respectively. Several key structural features accounting for the inhibitory activities of these compounds were discussed based on the 3D contour maps generated by the CoMFA and CoMSIA models, which were in good accordance with the docking results. These theoretical results rendered by 3D-QSAR models along with the docking may provide a useful reference for understanding the action mechanism and designing novel potential B-Raf inhibitors.     

QSAR Modeling,Molecular Docking and Cytotoxic Evaluation for Novel Oxidovanadium(IV) Complexes as Colon Anticancer Agents

Four new drug-based oxidovanadium (IV) complexes were synthesized and characterized by various spectral techniques, including molar conductance, magnetic measurements, and thermogravimetric analysis. Moreover, optimal structures geometry for all syntheses was obtained by the Gaussian09 program via the DFT/B3LYP method and showed that all of the metal complexes adopted a square-pyramidal structure. The essential parameters, electrophilicity (ω) value and expression for the maximum charge that an electrophile molecule may accept (ΔN_max) showed the practical biological potency of [VO(CTZ)₂] 2H₂O. The complexes were also evaluated for their propensity to bind to DNA through UV–vis absorption titration. The result revealed a high binding ability of the [VO(CTZ)₂] 2H₂O complex with K_b = 1.40 × 10⁶ M⁻¹. Furthermore, molecular docking was carried out to study the behavior of the VO (II) complexes towards colon cancer cell (3IG7) protein. A quantitative structure–activity relationship (QSAR) study was also implemented for the newly synthesized compounds. The results of validation indicate that the generated QSAR model possessed a high predictive power (R² = 0.97). Within the investigated series, the [VO(CTZ)₂] 2H₂O complex showed the greatest potential the most selective compound comparing to the stander chemotherapy drug.     

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.     

N-取代马来酰亚胺类hMGL抑制剂的3D-QSAR、分子对接和分子动力学研究

本文通过三维定量构效关系(3D-QSAR)建模、分子对接和分子动力学模拟,探讨了41个N-取代马来酰亚胺类衍生物与人单酰甘油脂肪酶(hMGL)的相互作用,并构建了相关模型。其中,比较分子力场分析模型(CoMFA、q₂ ^{= 0. 5}41、r₂ ^{= 0. 9}72)和比较分子相似性指数分析模型(CoMSIA、q₂ ^{= 0. 5}88、r₂ ^{= 0. 9}19)具有较好的预测能力。QSAR模型等势图阐明了该系列化合物生物活性与结构的关系,并依此设计了系列衍生物。采用分子对接和分子动力学模拟探讨了高活性化合物36、46与hMGL(PDB ID: 3PE6)的结合模式和稳定性,结果表明二者主要通过氢键和疏水相互作用与hMGL结合并且形成了稳定的复合物。随后对pIC₅₀预测值优于文献报道中最高活性化合物36的8个衍生物进行分子对接和ADMET预测,选择2个衍生物进行分子动力学模拟,结果表明2个衍生物分别与hMGL形成的复合物结合构象稳定。本研究为新型N-取代马来酰亚胺类单酰甘油脂肪酶抑制剂的开发提供了理论依据。     

Combretastatins类微管蛋白抑制剂的定量构效关系与结合模式

以Combretastatins的B环改造化合物为研究对象, 采用遗传函数分析方法进行了二维定量构效关系研究. 研究结果表明, Apol, PMI-mag, Dipole-mag, Hbond donor和RadOfGyration等描述符对该系列抑制剂活性的贡献最大. 采用比较分子场分析方法(CoMFA)和比较分子相似因子分析方法(CoMSIA)进行了三维定量构效关系研究, 建立的CoMFA和CoMSIA模型的交叉验证相关系数q2分别为0.630和0.634, 具有较强的预测能力. 利用CoMFA和CoMSIA模型的三维等势图解析了Combretastatins类化合物的构效关系, 阐明了B环上各取代基对抑制微管蛋白聚合活性的影响, 同时应用分子对接方法分析并验证了定量构效关系模型.     

基于分子对接的咪唑啉类α_(2A)-肾上腺素能配体三维定量构效关系研究

对系列咪唑啉类α2A-肾上腺素能配体类似物进行MFA和CoMFA两种方法的定量构效关系研究。化合物的构象采用与α2A-肾上腺素能受体分子对接的最佳结合构象,计算结果表明,两种方法的计算结果有较好的一致性。MFA模型的r2和xv_r2分别为0.997和0.946,CoMFA模型的r2和xv_r2分别为0.999和0.580,两种方法得到的定量构效关系模型均有较好的拟合能力和预测能力,可用于对咪唑啉类α2A-肾上腺素能配体化合物进行虚拟筛选和活性评价。研究结果为进一步设计合成新的候选化合物提供理论依据。     

五元杂环并嘧啶类胸苷酸合成酶抑制剂的构效关系和分子对接

用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)研究了38个五元杂环并嘧啶衍生物类胸苷酸合成酶抑制剂的三维定量构效关系(3D-QSAR), 建立了相关预测模型. CoMFA和CoMSIA模型的交互验证相关系数q²分别为0.662和0.672、非交互验证相关系数R²分别为0.921和0.884、外部交互验证相关系数Q_ext²分别为0.85和0.81. 分子对接得到的结合模式与三维定量构效关系得到的结果一致. 结果表明这两种模型都具有良好的预测能力, 可应用于指导化合物的设计和结构修饰, 为进一步设计新型胸苷酸合成酶抑制剂提供了理论依据.     

Synthesis,Biological Evaluation,and Molecular Docking of 1,4-Benzodioxan Derivatives as Potential Antibacterial Agents

Russian Journal of General Chemistry - A series of novel 1,4-benzodioxan derivatives containing Schiff base are synthesized by the method of splicing active substructures. All compounds are assayed...     

3C-like蛋白酶抑制剂的构效关系、分子对接和分子动力学

3C-like蛋白酶是中东呼吸综合征冠状病毒（MERS-CoV）等其它冠状病毒的繁殖过程中极为重要的蛋白酶。它已成为人类在抗冠状病毒领域中的研究热点。本文基于计算生物学方法对与MERS-CoV同属的蝙蝠冠状病毒HKU4（HKU4-CoV）的43个肽类3C-like蛋白酶抑制剂分子,建立三维定量构效关系（3D-QSAR）模型。在基于配体叠合的基础上,发现比较分子相似性指数分析法（CoMSIA）中的四个场组合（位阻场、静电场、氢键供体场与氢键受体场）为最优的模型（Q²=0.522,R_ncv²=0.996,R_pre²=0.904;Q²：交叉验证相关系数,R_ncv²：非交叉验证相关系数,R_pre²：验证集分子的预测值相关系数）,并借助该模型通过分子对接（docking）与分子动力学（MD）方法阐明了配受体结合作用。实验结果表明：（1）基于最优的CoMSIA模型基础上的三维等势图形象地说明了分子基团的位阻作用、静电作用、氢键供体与氢键受体作用对分子生物活性的影响;（2）分子对接研究结果显示了疏水性以及结晶水、氨基酸His166和Glu169在配体和受体结合过程中产生重要作用;（3）分子动力学模拟进一步验证了分子对接模型的可靠性,并发现了两个新的关键氨基酸Ser24与Gln192,它们与配体产生了两个较强的氢键。此外,根据这些结果,一些新的具有潜在抑制活性的肽类化合物作为3C-like蛋白酶抑制剂被获得。以上结果能够帮助深入了解3C-like蛋白酶与肽类抑制剂的作用机理,并且能够为今后的抗MERS-CoV药物设计提供有价值的参考。     

3-吡啶基醚类化合物的分子全息QSAR研究

采用分子全息定量构效关系(HQSAR, hologram quantitative structure-activity relationship)方法, 研究了28个3-吡啶基醚类化合物对乙酰胆碱α4β2受体的亲和性与它们的分子结构之间的关系, 讨论了分子碎片大小、分子碎片亚结构类型以及分子全息长度对QSAR的影响, 得到了较好的HQSAR模型, 模型的交叉验证系数平方q²＝0.670, 非交叉相关系数平方r²＝0.965, 偏差S＝0.093. 利用HQSAR的颜色编码, 对化合物中不同基团对亲和活性的影响进行了讨论, 对新配体的合成具有一定的指导作用.     

螺环吲哚类MDM2抑制剂的分子对接、定量构效关系和分子动力学模拟

采用分子对接、三维定量构效关系(3D-QSAR)和分子动力学方法研究了21个螺环吲哚类化合物与MDM2蛋白的相互作用, 并建立了相关预测模型. 比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)模型的交互验证相关系数q²分别为0.573 和0.651, 非交互验证相关系数r²分别为0.948和0.980. 分子对接得到的结合模式与分子动力学模拟得到的结果一致, 结合模式表明该类螺环吲哚化合物主要通过疏水相互作用和氢键与MDM2结合. 基于上述相互作用模型设计并合成了6个新结构螺环吲哚化合物, 并在MDM2高表达的前列腺癌LNCaP细胞株上测定其活性, 结果表明化合物5, 6的半数抑制浓度均低于1μg·mL^-1, 可作为新的抗肿瘤药物先导化合物进一步深入研究. 本研究对以MDM2为靶点的新结构螺环吲哚类抑制剂的开发提供了理论和实验依据.     

3D-QSAR和分子对接研究吲哚咔唑类细胞周期蛋白激酶抑制剂的选择性

细胞周期蛋白激酶(cyclin-dependent kinases, CDKs)是近年来治疗肿瘤的重要靶标. 由于大多数激酶ATP结合位点的保守性, CDK选择性激酶抑制剂的开发成为当前的研发难点和热点. 针对吲哚咔唑类CDK抑制剂, 我们采用比较分子力场分析方法(CoMFA)建立了CDK2-QSAR(quantitative structure-activity relationship)和CDK4-QSSR(quantitative structure-selectivity relationship)模型. 所建模型的交叉验证系数q2分别为0.722和0.703; 非交叉验证系数r2分别为0.977和0.946, 表明其具有较好的预测能力. 同时, 用分子对接的方法分析了这类化合物与CDK4同源模建结构的作用模式, 根据这两个模型发现, 吲哚咔唑类化合物的R5和R6位长链取代对CDK4的选择性具有一定的影响, 而且结合其作用模式比较合理地解释了这类抑制剂的选择性原因, 这对CDKs的选择性研究具有一定的指导意义.     

二氢嘧啶并[4,5-d]嘧啶类衍生物作为集落刺激因子-1受体激酶抑制剂的分子对接和定量构效关系研究

集落刺激因子-1受体激酶（CSF-1R）属于Ⅲ型受体酪氨酸激酶家族成员,其在调控单核巨噬细胞系中发挥重要作用。CSF-1R及其配体异常表达与肿瘤发展过程密切相关。因此,CSF-1R信号传导可成为抗肿瘤治疗的有吸引力的靶标。本文用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）研究了54个二氢嘧啶并[4,5-d]嘧啶类CSF-1R激酶抑制剂的三维定量构效关系（3D-QSAR）。基于配体叠合,CoMFA和CoMSIA模型的交叉验证系数（q2）分别为0.725和0.636,拟合验证系数（r2）分别为0.960和0.958,结果表明这两种模型均具有较好的预测能力。所建模型的等势图能直观反映分子不同取代基对活性的影响,其中立体场和疏水场对活性的贡献较大。通过分子对接研究显示,氨基酸残基Cys666、Asp796在配体和受体结合过程中产生作用,分子对接的结合模式与3D-QSAR得到的结果一致。这些信息为进一步优化CSF-1R激酶抑制剂提供了理论基础。     

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.     

2-(4-取代-苯基)-3-异噻唑啉酮抗菌活性的定量构效关系研究

用密度泛函理论和逐步回归分析方法对22种新合成的2-(4-取代-苯基)-3-异噻唑啉酮化合物进行了结构与抗菌活性研究. 通过对平衡几何构型、前线轨道组成和Mulliken电荷分布的分析得出: S(1), N(2)原子是该类化合物的主要活性部位, 且5位氯取代的化合物抗菌活性较好. 通过回归分析, 筛选了影响抗菌活性的主要因素, 建立了定量构效关系方程. 结果表明, 硫原子的亲核前线电子密度、3D-Balaban指数、S(1)-N(2) Wiberg键级和Schultz分子价拓扑指数是影响异噻唑啉酮类化合物抗大肠杆菌活性的主要因素, 所得模型对化合物抗菌活性有较好的预报能力.     

QSAR and Docking Studies of Thiazolidine-4-carboxylic Acid Derivatives as Neuraminidase Inhibitors

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. Here a quantitative structure activity relationship(QSAR) model was built by three-dimensional holographic atomic vector field(3 D-HoVAIF) and multiple linear regression(MLR). The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations. The correlation coefficient(R2) of established MLR model was 0.984, and the cross-validated correlation coefficient(Q2) of MLR model was 0.947. Furthermore, the cross-validated correlation coefficient for the test set(Qext2) was 0.967. The binding mode pattern of the compounds to the binding site of integrase enzyme was confirmed by docking studies. The results of present study indicated that this model can aid in designing more potent neuraminidase inhibitors.