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
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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。
Objective: Aminopeptidase N (APN), as known as CD13, is a member of zinc dependent metalloproteinase. It is widely expressed on the surface of renal and intestinal brush border cells, synaptic membranes in central systems and APN is over-expressed on the surface of some tumor cells. APN is demonstrated to play a key role in the process of tumor growth, invasion and metastasis. The functional aspects are briefly summarized: (1). Degrading extracellular matrix, promoting the growth and invasion of tumor cells; (2). As a regulator of novel vessels, promoting the angiogenesis of tumor tissues; (3). Degrading immunoactive substance and bioactive peptides, such as interleukin, thymopentin, enkephalin and so on.
More and more researches focus on the field of anti-aminopeptidase N drug as the enzyme has close relationship with the: some cancers. Most of the research on the anti-aminopeptidase N drug is to find aminopeptidase N inhibitor possessed inhibitory activity. Bestatin, the first marketed drug as aminopeptidase N inhibitor, now is clinically used to prolong the survival of patients with acute adult nonlymphcytic leukemia. In recent years, many natural aminopeptidase N inhibitors have been reported, such as Probestatin, Amastatin, Curcumin. Moreover, numerous synthetic APN inhibitors have also been reported, such asα-aminophosphonates APN inhibitors,β-amino- thiols APN inhibitors. In our group, we also had reported numerous synthetic small molecular peptidomimetic compounds targeted to aminopeptidase N to find novel scaffolds which possess potential aminopeptidase N inhibitory activities as anticancer lead compounds in the past ten years.
We used computer-aided drug design software to design and synthesize a series of compounds. Then we hope to find lead compounds with novel scaffold which possess potential aminopeptidase N inhibitory activity.
Methods: According to the active site of APN, the APN inhibitors can be divided into three parts. Part A: heterocyclic rings or hydrophobic groups; Part B: linker with Zinc-Binding-Group (ZBG); Part C: heterocyclic rings or hydrophobic groups. Part A and C was linked by Part B
First, four rational protein models of APN were built and tested by known active compounds. Secondly, the compounds which has zinc binding groups were picked out from NCI2000 database by SYBYL/Unity. Followly, these compunds were docked into the active site of APN models and the top ranked compounds were picked out. During this program, Bestatin (ID: 265489)、Phebestin (ID: 702307)、para-hydroxybestatin (ID: 327461) were taken as test compounds. At last, L-lysine derivates as our target compounds were built from top ranked compounds in docking and some known active compounds.
As a result, the target compounds are designed rationally and synthesized easily. In addition, enzyme assay, cancer cell (HL-60, ES-2, K562, A549, H7402, 2PLC) proliferation assay and in vivo experiment were processed in this research.
Results: We obtained 44 target compounds and all of them are novel without any report by now with the structures identified by IR, ~1H-NMR and ESI-MS.
Preliminary activity evaluation against APN showed that compound C7 was better than positive control Bestatin and C20 was equivalent. For D series, compounds D9 and D15 were better than positive control Bestatin and compound D24 was equivalent.
The results of in vitro growth inhibition against six tumor cells indicated that most potent APN inhibitors displayed good inhibitory effect against the growth of these tumor cells. Compounds C7、D9、D14、D15、D23 exhibited good potency against the proliferation of HL-60 cell line. Compounds C7、D14、D21、D23 showed better than Bestatin against ES-2 cell line and D23 was equivalent. Compound D21 showed better than Bestatin against H7402 cell line; and C7、D24 were equivalent. Compounds C7、C20、D9、D14、D15、D19、D21、D23、D24 showed better than Bestatin against PLC cell line.
The structure activity relationship (SAR) was summarized based on the structure and the activity data of the target compounds with the scaffold L-lysine. Comparative Molecular Field Analysis (CoFAR) was utilized to establish the quantitative structure activity relationship (QSAR) of the target compounds. The steric contour map and the electrostatic contour map of the CoMFA model showed the model had good cross-validated coefficient q~2 and predictive potency.
Finally, we obtain a complex of compound D24 and the mutational tricorn interacting factor F3 from thermoplasma acidophilum.
Conclusions: In conclusion, based on the virtual screening of APN, L-lysine derivates as APN inhibitors were designed and synthesized. We reported a convenient and economical method of the synthesis of APN inhibitors. Preliminary activity assays showed that most compounds displayed good APN inhibitory effect. Compounds C7、D9、D15 showed better than positive control Bestatin and could be used as lead compounds in the future. We also established a QASR model of target compounds which is beneficial for the design APN inhibitor in the future.
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。
Objective: Aminopeptidase N (APN), as known as CD13, is a member of zinc dependent metalloproteinase. It is widely expressed on the surface of renal and intestinal brush border cells, synaptic membranes in central systems and APN is over-expressed on the surface of some tumor cells. APN is demonstrated to play a key role in the process of tumor growth, invasion and metastasis. The functional aspects are briefly summarized: (1). Degrading extracellular matrix, promoting the growth and invasion of tumor cells; (2). As a regulator of novel vessels, promoting the angiogenesis of tumor tissues; (3). Degrading immunoactive substance and bioactive peptides, such as interleukin, thymopentin, enkephalin and so on.
More and more researches focus on the field of anti-aminopeptidase N drug as the enzyme has close relationship with the: some cancers. Most of the research on the anti-aminopeptidase N drug is to find aminopeptidase N inhibitor possessed inhibitory activity. Bestatin, the first marketed drug as aminopeptidase N inhibitor, now is clinically used to prolong the survival of patients with acute adult nonlymphcytic leukemia. In recent years, many natural aminopeptidase N inhibitors have been reported, such as Probestatin, Amastatin, Curcumin. Moreover, numerous synthetic APN inhibitors have also been reported, such asα-aminophosphonates APN inhibitors,β-amino- thiols APN inhibitors. In our group, we also had reported numerous synthetic small molecular peptidomimetic compounds targeted to aminopeptidase N to find novel scaffolds which possess potential aminopeptidase N inhibitory activities as anticancer lead compounds in the past ten years.
We used computer-aided drug design software to design and synthesize a series of compounds. Then we hope to find lead compounds with novel scaffold which possess potential aminopeptidase N inhibitory activity.
Methods: According to the active site of APN, the APN inhibitors can be divided into three parts. Part A: heterocyclic rings or hydrophobic groups; Part B: linker with Zinc-Binding-Group (ZBG); Part C: heterocyclic rings or hydrophobic groups. Part A and C was linked by Part B
First, four rational protein models of APN were built and tested by known active compounds. Secondly, the compounds which has zinc binding groups were picked out from NCI2000 database by SYBYL/Unity. Followly, these compunds were docked into the active site of APN models and the top ranked compounds were picked out. During this program, Bestatin (ID: 265489)、Phebestin (ID: 702307)、para-hydroxybestatin (ID: 327461) were taken as test compounds. At last, L-lysine derivates as our target compounds were built from top ranked compounds in docking and some known active compounds.
As a result, the target compounds are designed rationally and synthesized easily. In addition, enzyme assay, cancer cell (HL-60, ES-2, K562, A549, H7402, 2PLC) proliferation assay and in vivo experiment were processed in this research.
Results: We obtained 44 target compounds and all of them are novel without any report by now with the structures identified by IR, ~1H-NMR and ESI-MS.
Preliminary activity evaluation against APN showed that compound C7 was better than positive control Bestatin and C20 was equivalent. For D series, compounds D9 and D15 were better than positive control Bestatin and compound D24 was equivalent.
The results of in vitro growth inhibition against six tumor cells indicated that most potent APN inhibitors displayed good inhibitory effect against the growth of these tumor cells. Compounds C7、D9、D14、D15、D23 exhibited good potency against the proliferation of HL-60 cell line. Compounds C7、D14、D21、D23 showed better than Bestatin against ES-2 cell line and D23 was equivalent. Compound D21 showed better than Bestatin against H7402 cell line; and C7、D24 were equivalent. Compounds C7、C20、D9、D14、D15、D19、D21、D23、D24 showed better than Bestatin against PLC cell line.
The structure activity relationship (SAR) was summarized based on the structure and the activity data of the target compounds with the scaffold L-lysine. Comparative Molecular Field Analysis (CoFAR) was utilized to establish the quantitative structure activity relationship (QSAR) of the target compounds. The steric contour map and the electrostatic contour map of the CoMFA model showed the model had good cross-validated coefficient q~2 and predictive potency.
Finally, we obtain a complex of compound D24 and the mutational tricorn interacting factor F3 from thermoplasma acidophilum.
Conclusions: In conclusion, based on the virtual screening of APN, L-lysine derivates as APN inhibitors were designed and synthesized. We reported a convenient and economical method of the synthesis of APN inhibitors. Preliminary activity assays showed that most compounds displayed good APN inhibitory effect. Compounds C7、D9、D15 showed better than positive control Bestatin and could be used as lead compounds in the future. We also established a QASR model of target compounds which is beneficial for the design APN inhibitor in the future.
引文
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[4] SYBYL 7.0, Tripos Inc.: 1699 South Hanley Rd., St. Louis, Missouri, 63144.
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[7] BiopolymerTM is distributed by Tripos Inc., 1699 South Hanley Rd., St. Louis, Missouri, USA(http://www.tripos.com)