山荷叶素异羟肟酸和硫醇衍生物的合成与抑制肿瘤细胞增殖活性

通过药效团整合策略将天然抗肿瘤活性木脂素山荷叶素与组蛋白去乙酰化酶抑制剂的药效团长链异羟肟酸或长链硫醇进行拼合,得到了7个未见文献报道的山荷叶素衍生物,通过核磁共振氢谱、碳谱、高分辨质谱等方法进行了结构表征.并采用了噻唑蓝法(MTT)法对这些化合物的体外抑制肿瘤细胞增殖活性进行了测试,异羟肟酸类衍生物具有较强的活性.     

基于组蛋白去乙酰化酶靶点的羟肟酸类化合物及其抗肿瘤活性研究进展

组蛋白去乙酰化酶（histone deacetylase, HDACs）是肿瘤治疗的靶点之一,组蛋白去乙酰化酶抑制剂（histone deacetylase inhibitor, HDACi）可通过多种机制发挥抗肿瘤作用,包括抑制肿瘤细胞活力、迁移、侵袭、血管生成、增殖、DNA修复和诱导细胞凋亡。本文对近年来以HDACs为单靶点和多靶点的羟肟酸类衍生物的合成及其抗肿瘤活性进行综述,并对此方面的发展趋势、应用前景进行展望。     

组蛋白去乙酰化酶抑制剂的分子对接与构效关系研究

肿瘤是仅次于心血管疾病的第二大"杀手",肿瘤的发生与核心组蛋白的乙酰化及去乙酰化的失衡有密切的关系.这两种酶是一对功能相互拮抗的蛋白酶,分别对组蛋白氮端氨基酸残基进行乙酰化和去乙酰化调节染色质的结构,进而调控基因转录.组蛋白去乙酰酶抑制剂在体外和体内实验中均能引起乙酰化核小体组蛋白的堆积,提高p21基因的表达水平,抑制肿瘤细胞的增殖,诱导细胞分化或凋亡.研究组蛋白去乙酰化酶抑制剂和酶的分子对接可以阐明抑制剂与蛋白酶的作用模式,揭示抗癌机理.同时利用CoMFA方法研究了抑制剂结构和活性之间的关系,得到了预测能力较好的定量模型.     

环肽类组蛋白去乙酰化酶抑制剂

组蛋白乙酰化转移酶(HAT)和组蛋白去乙酰化酶(HDAC)调节组蛋白乙酰化程度，HDAC在基因表达和染色体形成等方面起着重要的调节作用。HDAC抑制剂能够引起肿瘤细胞生长停滞、诱导肿瘤细胞分化和调亡。通过对各种HDAC抑制剂结构及作用机制的研究有助于该类药物在临床上的应用和拓宽癌症治疗的适用范围。本文概述了近年来天然及合成的环肽类组蛋白去乙酰化酶抑制剂的研究进展。     

环肽类组蛋白去乙酰化酶抑制剂

组蛋白去乙酰化酶抑制剂是一类以组蛋白去乙酰化酶为靶点的新型靶向抗肿瘤药物,在抗增殖、促凋亡、促分化、阻滞细胞生长周期、抗血管生成等方面有很好的作用。组蛋白去乙酰化酶抑制剂以其独特的抗肿瘤作用机理,在研发肿瘤治疗药物中占有重要地位。在组蛋白去乙酰化酶抑制剂分类中,环肽类抑制剂结构最为复杂,对多种类型的实体瘤及血液学癌细胞均具有良好的对抗作用。本文针对天然和化学合成的环肽类组蛋白去乙酰化酶抑制剂中金属结合区、表面识别区以及连接区的结构特点进行了综述,并描述了各类抑制剂对酶的抑制活性和抗肿瘤增殖活性。对抑制剂不同结构区的修饰、改造可以使抑制剂对不同肿瘤细胞具有高效性和特异性作用,通过构效关系研究寻找具有高效低毒、靶向性环肽类抑制剂的结构规律,可为研究开发抗肿瘤药物提供帮助。     

基于药效团的血管紧张素转换酶抑制肽的结构优化

采用Discovery Studio2．0中的药效团模型生成方法,产生了基于化学特征的ACE抑制肽的药效团模型．所选择的认为最好的药效团模型（Hypo1）含有5个化学特征（1个阴离子中心、1个氢键受体、1个氢键给体、2个疏水中心）．我们先前采用实验的方法,从蚕蛹蛋白中获得具有ACE抑制活性的六肽分子,本文结合产生的ACE抑制肽药效团模型和分子对接研究,对该六肽分子进行结构优化,以识别六肽中对ACE抑制活性起关键作用的结构部分．结果显示,药效团模型的方法可有效用于ACE抑制肽的结构优化．     

一种香豆素荧光标记多肽的合成及应用

Sirtuin蛋白是一类称为依赖烟酰胺腺嘌呤二核苷酸（NAD）的组蛋白去乙酰化酶,共有7个成员,均是潜在的疾病治疗靶点。 然而,目前的荧光筛选方法,只适用于SIRT1~SIRT3。 因此,根据SIRT5的新酶活,设计、合成了针对SIRT5的荧光标记多肽（ISGASE(SuK) AMC）,并通过LC-MS和荧光检测证明了该荧光标记多肽能应用于SIRT5的活性筛选。     

组蛋白翻译后修饰无标定量方法可靠性的比较

组蛋白翻译后修饰是一种表观遗传学修饰,参与调控细胞的新陈代谢等重要生理过程。蛋白质组学发展迅速,使监控组蛋白翻译后修饰的动态变化成为可能。目前主要有3种无标定量方法（谱图计数法、峰面积积分法和信号强度法）,但何种定量方法更可靠尚未见系统性的详细报道。在稳定同位素标记细胞培养技术（SILAC）基础上,对去乙酰化酶抑制剂（SAHA）调控细胞乙酰化修饰水平的定量数据进行对比,比较3种无标定量方法对组蛋白翻译后修饰进行的定量分析,利用定量结果的标准差（SD）评估定量的可靠性,最终发现基于峰面积积分法定量的结果可靠性最高。该研究对难以进行同位素标记实验的样本分析,尤其对临床样本、大样本的组蛋白修饰谱分析具有重要参考意义。     

磺胺基羟肟酸类HDAC抑制剂三维定量构效关系

组蛋白去乙酰化酶(HDAC)对染色质分布和基因调节起着重要的作用,也是治疗癌症和其它疾病的新靶点.羟肟酸类抑制剂是目前研究最多的组蛋白去乙酰化酶抑制剂.应用比较分子力场(CoMFA)法对一系列磺胺基羟肟酸类HDAC抑制剂进行了结构活性关系研究,得到的模型具有较高的交叉验证系数(q²=0.704).并在此基础上,建立了非交叉验证的偏最小二乘分析(PLS)模型.用该模型对随机选择的6个化合物组成的测试集进行了预测,得到了令人满意的结果,所建模型具有良好的预测能力.本研究对于设计高活性的HDAC抑制剂及抗癌药物都有指导意义.     

新型酪氨酸激酶小分子抑制的三维药效团研究

通过CATALYST软件包得到了两类HER2抑制的三维药效团模型。尽管亚苄基丙二腈化合物和3-取代吲哚啉-2-酮系列化合物具有完全不同的骨架结构,但得到的药效团却具有共同的特性,这表明当这两类抑制剂和受体发生相互作用时,采用了相似的结合模式。共同的药效团模型包括一个氢键受体,一个氢键给体,一个脂肪类疏水团以及一个芳香类疏水团。根据药效团模型,我们还进行了三维构效关系的研究,结果表明得到的药效团模型具有很好的预测能力(线性回归系数R≈0.96)。药效团模型对于研究酪氨酸激酶小分子抑制剂的结构与活性关系,以及评估和预测此类未知化合物活性具人重要的意义。     

The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

A three-dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti-cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis （Hypo 1） was defined by four features： one hydrogen-bond donor and three hy- drophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross-validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.     

Pharmacophore Modeling and in Silico Screening Studies to Design Potential KDR Kinase Inhibitors

A novel ligand‐based pharmacophore model for KDR kinase was generated on the basis of chemical features of 30 KDR kinase inhibitors. This pharmacophore model consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor and two hydrophobic groups. Several methods have been used to validate the model, suggesting that it can serve as a reliable tool for virtual screening to facilitate the discovery of novel KDR inhibitors. The model was then used as database search query from the National Cancer Institute (NCI) database for the rational design to identify new hit compound.     

Construction of the Pharmacophore Model of Glycogen Synthase Kinase-3 Inhibitors

A three dimensional pharmacophore model has been generated for glycogen synthase kinase-3 （GSK-3） inhibitots. A dataset consisting of 89 compounds was selected on the basis of the information content of the structures and activity data as required by the CATALYST system. The optimum model with four features （one hydrogen-bond acceptor unit, one ring aromatic unit, and two hydrophobic aromatic units） was selected with a good correlation coefficient （0.95）. This model is able to predict the activity of other known GSK-3 inhibitors not included in the model generation, and can be further used to identify structurally diverse compounds with desired biological activity by virtual screening.     

α1A-亚型肾上腺素受体拮抗剂3D药效团模型的研究

运用Catalyst软件以34个α_1A-AR拮抗剂分子为训练集, 构建了包含一个氢键受体、一个正电中心和一个芳环中心的三元素药效团模型, 线性回归相关系数为0.89. 经13个分子组成的测试集验证该药效团模型具有较好的活性预测能力, 为寻找新的α_1A-AR拮抗剂分子提供了理论基础.     

Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

HMG-CoA还原酶抑制剂三维药效团的构建

以作用于鼠肝脏细胞的21个3-羟基-3-甲基戊二酰辅酶A(HMG-CoA)还原酶抑制剂(RI)为训练集, 训练集化合物具备结构多样性, 来源于相同药理模型, 活性值IC50范围在0.3-8000 nmol·L-1. 利用Catalyst 计算HMG-CoA还原酶抑制剂最优药效团由一个氢键受体, 一个氢键给体, 一个疏水基团和一个芳香环特征组成. 药效团模型Fixed cost值, Total cost值和Configuration cost值分别为88.75、111.5 和16.98. 训练集化合物活性计算值与实测值相关系数为0.8883, 偏差值为1.269, 交叉验证结果表明, 药效团模型具有较高的置信度, 对测试集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索发现新的具有该活性的化合物, 也可用于中药或天然产物药物的研究开发.     

Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products

Matrix metalloproteinase-9 (MMP-9) is an attractive target for cancer therapy. In this study, the pharmacophore model of MMP-9 inhibitors is built based on the experimental binding structures of multiple receptor-ligand complexes. It is found that the pharmacophore model consists of six chemical features, including two hydrogen bond acceptors, one hydrogen bond donor, one ring aromatic regions, and two hydrophobic (HY) features. Among them, the two HY features are especially important because they can enter the S1′ pocket of MMP-9 which determines the selectivity of MMP-9 inhibitors. The reliability of pharmacophore model is validated based on the two different decoy sets and relevant experimental data. The virtual screening, combining pharmacophore model with molecular docking, is performed to identify the selective MMP-9 inhibitors from a database of natural products. The four novel MMP-9 inhibitors of natural products, NP-000686, NP-001752, NP-014331, and NP-015905, are found; one of them, NP-000686, is used to perform the experiment of in vitro bioassay inhibiting MMP-9, and the IC₅₀ value was estimated to be only 13.4 µM, showing the strongly inhibitory activity of NP-000686 against MMP-9, which suggests that our screening results should be reliable. The binding modes of screened inhibitors with MMP-9 active sites were discussed. In addition, the ADMET properties and physicochemical properties of screened four compounds were assessed. The found MMP-9 inhibitors of natural products could serve as the lead compounds for designing the new MMP-9 inhibitors by carrying out structural modifications in the future.     

Accounting of ligand–receptor interactions to explore and design novel architecture for PTP 1B inhibition: a legitimate approach

Computer‐aided drug design was performed on a diverse set of 103 biphenyl derivatives that demonstrated antidiabetic activity by restraining the protein tyrosine phosphatase 1B (PTP 1B) receptor. A four‐point pharmacophore hypothesis using the PHASE module of Schrödinger suite with one hydrogen bond acceptor (A) and three aromatic rings (R) as pharmacophoric features was generated. The hypothesis, ARRR.2, considered the best hypothesis in the present study is characterized by survival score (3.553), cross‐validated r² (Q²) (0.722), regression coefficient (0.949), Pearson R (0.867), and F value (492.6). The developed pharmacophore model was externally validated by predicting the activity of test set molecules. Docking algorithm combined with the drug–receptor binding free energetic and pharmacokinetic drug profile envisaged a novel concept, which may provide structural insights for the development of potential PTP 1B inhibitors. The study also provided a valid rapport between pharmacophore drug mapping, atom‐based three‐dimensional quantitative structure–activity relationship, molecular docking, sitemap, molecular simulations, and pharmacokinetic prediction approaches demonstrating the trends in activity. The results of these ligand–receptor relationship studies may account to design a legitimate template for the development and optimization of highly selective and potent PTP 1B inhibitors. Copyright © 2012 John Wiley & Sons, Ltd.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.