类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

doi:10.7503/cjcu20160643

高等学校化学学报 ›› 2017, Vol. 38 ›› Issue (1): 41.doi: 10.7503/cjcu20160643

类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

刘本国¹, 刘江伟¹, 李嘉琪², 耿升¹, 莫海珍¹, 梁桂兆²()

1. 河南科技学院食品学院, 新乡 453003
2. 重庆大学生物工程学院, 生物流变科学与技术教育部重点实验室, 重庆 400044

收稿日期:2016-09-14 出版日期:2017-01-10 发布日期:2016-12-20
作者简介:联系人简介: 梁桂兆, 男, 博士, 教授, 博士生导师, 主要从事生物医学与健康食品研究. E-mail: gzliang@cqu.edu.cn
基金资助:
国家自然科学基金(批准号: 31571782, 21166024)、河南省高校科技创新团队(批准号: 16IRTSTHN007)、河南省高校科技创新人才项目(批准号: 14HASTIT019)和河南省基础与前沿技术研究计划项目(批准号: 162300410177)资助.

3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors^†

LIU Benguo¹, LIU Jiangwei¹, LI Jiaqi², GENG Sheng¹, MO Haizhen¹, LIANG Guizhao^2,^*()

1. School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
2. Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College,Chongqing University, Chongqing 400044, China

Received:2016-09-14 Online:2017-01-10 Published:2016-12-20
Contact: LIANG Guizhao E-mail:gzliang@cqu.edu.cn
Supported by:
† Supported by the National Natural Science Foundation of China(Nos.31571782, 21166024), the Program for Innovative Research Team(in Science and Technology) in University of Henan Province, China(No.16IRTSTHN007), the Program for Science & Technology Innovation Talents in Universities of Henan Province, China(No.14HASTIT019) and the Foundation of Research and Advanced Technology of Henan Province, China(No.162300410177).

摘要/Abstract

摘要：

采用基于R基团搜索技术的Topomer CoMFA建立了30个类黄酮类P糖蛋白抑制剂的三维定量构效关系(3D-QSAR)模型, 并用包括9个样本的测试集验证模型的外部预测能力. 所得模型的拟合、交互验证以及外部验证的复相关系数分别为r²=0.971, q²=0.728和 $r pred 2$ =0.816. 在此基础上, 运用Surflex-dock分子对接法研究了白杨素及其异戊烯化衍生物与P糖蛋白的作用模式. 结果表明, 异戊烯化修饰可显著提高类黄酮的亲脂性, 修饰产物能更好地与P糖蛋白的疏水性口袋契合, 二者结合程度高.

关键词: 类黄酮, P糖蛋白, 三维定量构效关系, Topomer CoMFA, Surflex-dock

Abstract:

A 3D-QSAR model of 30 flavonoids as P-glycoprotein inhibitors was constructed using Topomer CoMFA. Nine flavonoids were used to validate the external predictive power of the obtained model. The multiple correlation coefficients of fitting, cross validation and external validation were 0.971, 0.728 and 0.816, respectively. The molecular docking modes of chrysin and its prenylated derivatives were established by the Surflex-dock method. The results showed the lipophilicity of flavonoids could be improved by the prenyl modification, which resulted to the stronger interaction with the hydrophobic pocket of P-glycoprotein.

Key words: Flavonoids, P-glycoprotein, 3D-Quantitative structure-activity relationship, Topomer CoMFA, Surflex-dock

中图分类号:

O621
R914

TrendMD:

刘本国, 刘江伟, 李嘉琪, 耿升, 莫海珍, 梁桂兆. 类黄酮抑制P糖蛋白的三维定量构效关系与作用模式. 高等学校化学学报, 2017, 38(1): 41.

LIU Benguo, LIU Jiangwei, LI Jiaqi, GENG Sheng, MO Haizhen, LIANG Guizhao. 3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors^†. Chem. J. Chinese Universities, 2017, 38(1): 41.

图/表 8

Fig.1 Chemical structures of flavonoids

Table 1 Bioactivity data of 3a flavonoids

Compd.	Type	3	4	5	6	7	8	2'	3'	4'	5'	6'	K_D/ (μmol·L^-1)	Group
1	Ⅰ	—H		—H	—H	—OH	—H	—H	—H	—H	—H	—H	34.900	Training
2	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	10.100	Training
3	Ⅰ	—OH		—H	—H	—H	—H	—H	—H	—H	—H	—H	10.100	Training
4	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Test
5	Ⅰ	—CH₃		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Training
6	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—OH	—OH	—H	—H	7.000	Training
7	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—F	—H	—H	6.800	Training
8	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	6.700	Test
9	Ⅰ	—H		—OH	—H	—OH	—H	—H	—F	—F	—H	—H	6.300	Training
10	Ⅰ	—H		—OH	—H	—OCH₃	—H	—H	—H	—H	—H	—H	6.300	Training
11	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	5.300	Training
12	Ⅱ	—H	—OH	—H	—H			—OH	—H	—OH	—H	—OH	4.800	Test
13	Ⅱ	—H	—H	—H	—H			—OH	—H	—OH	—H	—OH	4.600	Training
14	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OCH₃	—H	—H	4.500	Training
15	Ⅰ	—OH		—OH	—H	—OH	—H	—Cl	—H	—Cl	—H	—H	4.000	Training
16	Ⅱ	—H	—F	—H	—H			—OH	—H	—OH	—H	—OH	3.600	Test
17	Ⅰ	—H		—OH	—CH₃	—OH	—H	—H	—H	—H	—H	—H	3.100	Training
18	Ⅱ	—H	—OCH₃	—H	—H			—OH	—H	—OH	—H	—OH	2.300	Training
19	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	2.200	Training
20	Ⅰ	—H		—OH	—CH₃	—CH₃	—H	—H	—H	—H	—H	—H	1.300	Test
21	Ⅱ	—H	—Cl	—H	—H			—OH	—H	—OH	—H	—OH	1.300	Training
22	Ⅰ	—H		—OH	—H	Isopropyl	—H	—H	—H	—H	—H	—H	1.300	Training
23	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	1.100	Training
24	Ⅰ	—H		—OH	—H	—OH	Benzyl	—H	—H	—H	—H	—H	0.990	Test
25	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—OH	—H	—H	0.700	Training
26	Ⅱ	—H	—Br	—H	—H			—OH	—H	—OH	—H	—OH	0.570	Training
27	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.450	Training
28	Ⅰ	—H		—OH	Benzyl	—OH	—H	—H	—H	—H	—H	—H	0.340	Test
29	Ⅰ	—H		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.300	Training
30	Ⅰ	—H		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.280	Training
31	Ⅱ	—H	—I	—H	—H			—OH	—H	—OH	—H	—OH	0.250	Training
32	Ⅰ	—OH		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.220	Test
33	Ⅰ	—OH		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.210	Training
34	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—OCH₃	—H	—H	0.200	Training
35	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.200	Training
36	Ⅰ	—CH₃		—OH	—H	—CH₃	Dimethylallyl	—H	—H	—H	—H	—H	0.150	Test
37	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	n-C₈H₁₇	—H	—H	0.060	Training
38	Ⅰ	—H		—OH	Geranyl	—OH	—H	—H	—H	—H	—H	—H	0.045	Training
39	Ⅰ	—H		—OH	—H	—OH	Geranyl	—H	—H	—H	—H	—H	0.025	Training

Table 1 Bioactivity data of 3a flavonoids

Compd.	Type	3	4	5	6	7	8	2'	3'	4'	5'	6'	K_D/ (μmol·L^-1)	Group
1	Ⅰ	—H		—H	—H	—OH	—H	—H	—H	—H	—H	—H	34.900	Training
2	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	10.100	Training
3	Ⅰ	—OH		—H	—H	—H	—H	—H	—H	—H	—H	—H	10.100	Training
4	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Test
5	Ⅰ	—CH₃		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	8.900	Training
6	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—OH	—OH	—H	—H	7.000	Training
7	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—F	—H	—H	6.800	Training
8	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OH	—H	—H	6.700	Test
9	Ⅰ	—H		—OH	—H	—OH	—H	—H	—F	—F	—H	—H	6.300	Training
10	Ⅰ	—H		—OH	—H	—OCH₃	—H	—H	—H	—H	—H	—H	6.300	Training
11	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—H	—H	—H	5.300	Training
12	Ⅱ	—H	—OH	—H	—H			—OH	—H	—OH	—H	—OH	4.800	Test
13	Ⅱ	—H	—H	—H	—H			—OH	—H	—OH	—H	—OH	4.600	Training
14	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—OCH₃	—H	—H	4.500	Training
15	Ⅰ	—OH		—OH	—H	—OH	—H	—Cl	—H	—Cl	—H	—H	4.000	Training
16	Ⅱ	—H	—F	—H	—H			—OH	—H	—OH	—H	—OH	3.600	Test
17	Ⅰ	—H		—OH	—CH₃	—OH	—H	—H	—H	—H	—H	—H	3.100	Training
18	Ⅱ	—H	—OCH₃	—H	—H			—OH	—H	—OH	—H	—OH	2.300	Training
19	Ⅰ	—H		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	2.200	Training
20	Ⅰ	—H		—OH	—CH₃	—CH₃	—H	—H	—H	—H	—H	—H	1.300	Test
21	Ⅱ	—H	—Cl	—H	—H			—OH	—H	—OH	—H	—OH	1.300	Training
22	Ⅰ	—H		—OH	—H	Isopropyl	—H	—H	—H	—H	—H	—H	1.300	Training
23	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	—I	—H	—H	1.100	Training
24	Ⅰ	—H		—OH	—H	—OH	Benzyl	—H	—H	—H	—H	—H	0.990	Test
25	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—OH	—H	—H	0.700	Training
26	Ⅱ	—H	—Br	—H	—H			—OH	—H	—OH	—H	—OH	0.570	Training
27	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.450	Training
28	Ⅰ	—H		—OH	Benzyl	—OH	—H	—H	—H	—H	—H	—H	0.340	Test
29	Ⅰ	—H		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.300	Training
30	Ⅰ	—H		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.280	Training
31	Ⅱ	—H	—I	—H	—H			—OH	—H	—OH	—H	—OH	0.250	Training
32	Ⅰ	—OH		—OH	—H	—OH	Prenyl	—H	—H	—H	—H	—H	0.220	Test
33	Ⅰ	—OH		—OH	Prenyl	—OH	—H	—H	—H	—H	—H	—H	0.210	Training
34	Ⅰ	—OH		—OH	—H	—OH	Dimethylallyl	—H	—H	—OCH₃	—H	—H	0.200	Training
35	Ⅰ	—H		—OH	—H	—OH	Dimethylallyl	—H	—H	—H	—H	—H	0.200	Training
36	Ⅰ	—CH₃		—OH	—H	—CH₃	Dimethylallyl	—H	—H	—H	—H	—H	0.150	Test
37	Ⅰ	—OH		—OH	—H	—OH	—H	—H	—H	n-C₈H₁₇	—H	—H	0.060	Training
38	Ⅰ	—H		—OH	Geranyl	—OH	—H	—H	—H	—H	—H	—H	0.045	Training
39	Ⅰ	—H		—OH	—H	—OH	Geranyl	—H	—H	—H	—H	—H	0.025	Training

Fig.2 Linear regression between the observed and predicted pKD of 39 flavonoids

Table 2 Comparison of QSAR modeling of flavonoids as P-glycoprotein inhibitors

No.	Modeling method	Training set size	Test set size	r²	q²	$r pred 2$
1	2D-QSAR^[11]	46	0	0.912	0.800
2	2D-QSAR^[12]	43	14	0.826	0.721	0.679
3	Ligand based CoMFA^[21]	32	9	0.747	0.639	0.802
4	Ligand based CoMSIA^[21]	32	9	0.810	0.676	0.785
5	Receptor guided CoMFA^[21]	32	9	0.712	0.497	0.841
6	Receptor guided CoMSIA^[21]	32	9	0.805	0.589	0.937
7	Topomer CoMFA	30	9	0.971	0.728	0.816

Table 2 Comparison of QSAR modeling of flavonoids as P-glycoprotein inhibitors

No.	Modeling method	Training set size	Test set size	r²	q²	$r pred 2$
1	2D-QSAR^[11]	46	0	0.912	0.800
2	2D-QSAR^[12]	43	14	0.826	0.721	0.679
3	Ligand based CoMFA^[21]	32	9	0.747	0.639	0.802
4	Ligand based CoMSIA^[21]	32	9	0.810	0.676	0.785
5	Receptor guided CoMFA^[21]	32	9	0.712	0.497	0.841
6	Receptor guided CoMSIA^[21]	32	9	0.805	0.589	0.937
7	Topomer CoMFA	30	9	0.971	0.728	0.816

Fig.3 3D contour plots of the Topomer CoMFA modelThe reference molecule used was sample 4(Chrysin). (A) Steric field map of R1-group; (B) electrostatic field map of R1-group;(C) steric field map of R2-group; (D) electrostatic field map of R2-group.

Fig.4 Docked pose corresponding to the minimum energy conformation for compound 4(A, B) and compound 39(C, D) binding to P-glycoprotein

Fig.5 MOLCAD of lipophilicity in binding pocket of 3G60

Table 3 Total score and ClogP values of chrysin and its prenylated derivatives

Molecules	Compd.	Total score	ClogP	H-donor	H-acceptor	M_w
Chrysin	4	2.69	3.56	2	4	254.24
6-Prenyl-chrysin	29	3.87	5.46	2	4	322.36
8-Prenyl-chrysin	30	4.06	5.51	2	4	322.36
6-Geranyl-chrysin	38	6.66	7.49	2	4	390.48
8-Geranyl-chrysin	39	7.47	7.54	2	4	390.48

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类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors^†

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类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors†

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3D-QSAR and Interaction Mechanism of Flavonoids s P-glycoprotein Inhibitors^†