基于L-赖氨酸骨架的APN抑制剂的计算机辅助设计、合成及活性研究

发布时间：2018-03-08 05:13

本文选题：APN/CD13　切入点：虚拟筛选　出处：《山东大学》2009年博士论文　论文类型：学位论文

【摘要】： 目的:氨肽酶N(Aminopeptidase N,APN),也被称为CD13,是一类锌离子依赖性金属蛋白酶,广泛存在于小肠、肾及中枢神经系统的多种细胞表面。与正常细胞相比,该酶在肿瘤细胞表面高水平表达。研究发现,APN在肿瘤生长、侵袭和转移过程中发挥着重要作用。例如,APN可降解细胞外基质,促进原发肿瘤的生长侵袭,有利于肿瘤的转移;同时APN还可以促进肿瘤新生血管的形成,是肿瘤新生血管的调节器;另外该酶还能够降解胸腺肽和白介素,从而降低机体免疫机能。正因为APN与肿瘤的密切关系,APN抑制剂的研究已经成为抗肿瘤研究领域的一个热点。Bestatin作为第一个上市的APN抑制剂,临床上主要用于治疗急性成人非淋巴性白血病。近年又发现许多天然APN抑制剂,如Probestatin,Amastatin,Curcumin等;另外,人们还合成了许多小分子化合物APN抑制剂,如α-氨基磷酸抑制剂,β-氨基硫醇类抑制剂等。本课题组以APN为靶点,经十几年的研究亦报道了许多小分子类肽化合物。本研究以APN为靶点,在充分调研文献的基础上,通过计算机辅助药物设计技术,设计、合成一系列以L-赖氨酸为基本骨架的小分子化合物,并对它们进行初步的活性筛选,以期发现具有较好的APN抑制活性的先导化合物。方法:由APN的晶体结构及抑制剂与酶的作用模式可以看出与APN活性位点相对应,对应的APN抑制剂应由三个部分组成:A疏水性芳环侧链;B锌离子螯合基团位于中间连接片段上;C疏水性芳环侧链。A和C两个疏水侧链经B部分相连。本研究利用已知的APN抑制剂建立合理的APN受体评价模型,通过常见的锌离子螯合基团,采用SYBYL/Unity模块从NCI2000化合物库中挑选出具有锌离子螯合基团的化合物。而后将这些化合物与所建立的APN受体评价模型进行对接,挑选出打分较高的化合物作为中靶化合物。在此过程中,库中原有的APN抑制剂Bestatin(ID:265489)、Phebestin(ID:702307)、para-hydroxybestatin(ID:327461)作为测试分子以验证方法的有效性。参考中靶化合物和一些已知的APN抑制剂,进而设计了一系列结构全新的以L-赖氨酸为骨架的目标化合物。本研究将目标化合物进行了体外抑酶实验、体外肿瘤细胞(HL-60,ES-2,K562,A549,H7402,PLC)生长抑制以及体内抗肿瘤转移实验,从中筛选出具有APN抑制活性的抗癌先导化合物。结果:本文共设计并合成了44个目标化合物,并对所有化合物通过红外光谱、核磁共振氢谱、电喷雾质谱等方法进行了结构确证。经查阅文献证实,所合成的目标化合物为新型化合物,未见文献报道。在C系列化合物中,化合物C7的抑酶活性优于两个阳性对照药B6和Bestatin,化合物C20的抑酶活性与它们相近。在D系列化合物中,化合物D9、D15的抑酶活性优于两个阳性对照药B6和Bestatin,化合物D24的抑酶活性与二者相近。 MTT法体外细胞实验测定了目标化合物对HL-60人白血病细胞株、ES-2卵巢透明癌细胞株、K562人白血病细胞株、A549人肺腺癌细胞株、H7402人肝癌细胞株及PLC人肝癌细胞株的生长抑制作用,结果显示对APN酶抑制作用较强的化合物C7、D9、D14、D15、D23同样有很强的HL-60细胞抑制活性,超过了阳性对照药Bestatin;化合物D14、D21的ES-2细胞抑制活性优于阳性对照,D23与之相当;化合物D21的H7402细胞抑制活性优于阳性对照,C7、D24与之相当;化合物C7、C20、D9、D14、D15、D19、D21、D23、D24的PLC细胞抑制活性均优于阳性对照。本文在虚拟筛选的基础上,以所设计、合成的化合物为对象,利用计算机软件进行了初步的定量构效关系总结。采用比较分子场分析方法(CoMFA)建立了L-赖氨酸类APN抑制剂的定量构效关系(QSAR)模型。通过对CoMFA模型立体场等势线图和静电场等势线图的分析,建立的COMFA模型具有较高的交叉验证系数q~2和一定的预测能力。最后,我们还得到一个化合物D24与突变嗜酸热源菌三角交互作用因子F3的共结晶复合物。结论:本研究基于APN的晶体结构及抑制剂与酶的作用模式,利用计算机软件进行设计、合成的L-赖氨酸类化合物具有很好的APN抑制活性。所设计的合成路线科学合理,原料经济易得。通过初步的活性测试发现了具有进一步研究价值的抗癌先导化合物。其中,化合物C7、D9、D15的抑酶活性优于阳性对照药Bestatin,可作为先导化合物用于指导下一轮的结构优化。此外,我们基于化合物结构和活性数据建立了有一定预测能力的定量构效关系模型,为今后新型氨肽酶抑制剂的研究奠定了基础。
[Abstract]:Objective: aminopeptidase N (Aminopeptidase N APN), also known as CD13, is a zinc dependent metalloproteinase, widely exists in the small intestine, the surface of kidney and central nervous system cells. Compared with normal cells, the expression of this enzyme in tumor cells in high level. The study found that APN in tumor growth, plays an important role in the process of invasion and metastasis. For example, APN can degrade extracellular matrix, promoting the growth and invasion of primary tumor, metastasis to tumor; while APN can promote tumor angiogenesis, is a regulator of tumor blood vessels; in addition the enzyme can also degrade thymosin and interleukin, thereby reducing immune function.
Because of the close relationship between APN and tumor, APN inhibitors have become the field of anti-tumor research a hot.Bestatin as the first listed APN inhibitor, is used clinically for the treatment of adult acute non lymphatic leukemia. In recent years, and found that many natural APN inhibitors, such as Probestatin, Amastatin, Curcumin and so on; in addition, people also the synthesis of many small molecules such as APN inhibitors, alpha amino acid inhibitors, beta amino thiol inhibitors. This paper takes APN as the target, after ten years of research also reported a number of small molecular peptide compounds.
This study takes APN as the target, based on a detailed survey of literature, through the design of computer aided drug design, synthesis technology, a series of small molecules with L- lysine as the basic skeleton of the compounds, and they are preliminary screening activity, in order to find lead compounds with better APN inhibitory activity.
Methods: the crystal structure and the mode of action of APN inhibitors and the enzyme can be seen corresponding to the active site of APN, the corresponding APN inhibitors should be composed of three parts: A hydrophobic aromatic side chain; B zinc ion chelating group is located in the middle of the junction fragment; C hydrophobic aromatic side chains of.A and C two hydrophobic side chains are connected via B.
This study uses APN APN receptor inhibitor known to establish reasonable evaluation model, the zinc ion chelating group common, using SYBYL/Unity module selected compounds with zinc ion chelating groups from NCI2000 compound library. And then the APN receptor evaluation of these compounds and the model for docking, pick out as high scoring compounds the target compounds. In this process, the original APN inhibitor Bestatin Library (ID:265489), Phebestin (ID:702307), para-hydroxybestatin (ID:327461) as a test to verify the validity of the molecular method. APN inhibitor compounds and some known reference target, and then designed a series of novel L- lysine as target compounds skeleton.
The aim of this study is to inhibit the enzyme activity in vitro, and inhibit tumor growth in vitro (HL-60, ES-2, K562, A549, H7402, PLC), and in vivo anti-tumor metastasis experiment, and screened out an anticancer lead compound with APN inhibitory activity.
Results: 44 target compounds were designed and synthesized, and all compounds were identified by IR, 1H-NMR and electrospray ionization mass spectrometry.
In the C series of compounds, the enzyme activity of two is better than that of the positive control drug B6 and Bestatin under compound C7, compound C20 inhibitory activity and are similar. The D series compounds, compound D9, enzyme activity of two is better than that of the positive control drug B6 and Bestatin under D15, inhibitory activity of compound D24 is similar two.
The target compounds on human leukemia cell line HL-60 was determined by MTT in vitro, ES-2 ovarian clear cell carcinoma cell line K562, human leukemia cell lines, human lung adenocarcinoma cell line A549, growth inhibition of human hepatocellular carcinoma cell line H7402 and PLC in human hepatocellular carcinoma cell line, showed inhibitory compounds C7, strong effect on APN D14, D15, D9, D23 also has a strong HL-60 cell inhibitory activity than positive control drug Bestatin; compound D14, D21 ES-2 cell inhibitory activity than the positive control, D23 equivalent; compound D21 H7402 cell inhibitory activity than the positive control, C7, D24 and a compound C7; C20, D9, D14, D15, D19, D21, D23, D24, PLC cell inhibitory activity was better than the positive control.
Based on the virtual screening on the design, synthesis of compounds as the object, has carried on the preliminary quantitative structure-activity relationship by using computer software. Using comparative molecular field analysis (CoMFA) to establish a quantitative L- lysine APN inhibitor structure-activity relationship (QSAR) model. Through the analysis of contour maps the CoMFA model of steric contour map and the electrostatic field, the COMFA model has a higher coefficient of cross validation q~2 and predictive ability.
Finally, we also obtained a co crystallization complex of a compound D24 and the trigonometric interaction factor F3 of the mutant eosinophilic bacteria.
Conclusion: This study based on the crystal structure and the mode of action of APN inhibitors and enzymes, were designed using computer software, the synthesis of L- amino acid compound has good inhibitory activity on APN. The synthetic route design is scientific and reasonable, economical and easy to get raw materials. Through the activity of the preliminary test is found to have anticancer lead compounds for further study on value. Among them, compounds C7, D9, better than the activity of the positive control drug Bestatin under D15, can be used as a lead compound for structural optimization under the guidance of a wheel. In addition, we compound structure and activity data based on a quantitative prediction ability of the QSAR model, laid a foundation for further research of new aminopeptidase inhibitors.

【学位授予单位】：山东大学
【学位级别】：博士
【学位授予年份】：2009
【分类号】：R341

【引证文献】