基于药效团模型的人多药耐药相关蛋白内源性底物的预测

doi:10.5246/jcps.2017.08.061

中国药学(英文版) ›› 2017, Vol. 26 ›› Issue (8): 545-555.DOI: 10.5246/jcps.2017.08.061

• 【研究论文】 • 下一篇

基于药效团模型的人多药耐药相关蛋白内源性底物的预测

刘园, 陈亚, 胡建星, 刘振明^*, 张亮仁^*

北京大学医学部药学院天然药物与仿生药物国家重点实验室, 北京 100191

收稿日期:2017-05-15 修回日期:2017-06-12 出版日期:2017-08-31 发布日期:2017-06-30
通讯作者: Tel.: +86-010-82802567; +86-010-82805514, E-mail: zmliu@bjmu.edu.cn; liangren@bjmu.edu.cn
基金资助:
The National Natural Science Foundation of China (Grant No. 21272017 and 21572010).

In silico pharmacophore models to predict endogenous substrates for human multidrug resistance-associated proteins

Yuan Liu, Ya Chen, Jianxing Hu, Zhenming Liu^*, Liangren Zhang^*

State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China

Received:2017-05-15 Revised:2017-06-12 Online:2017-08-31 Published:2017-06-30
Contact: Tel.: +86-010-82802567; +86-010-82805514, E-mail: zmliu@bjmu.edu.cn; liangren@bjmu.edu.cn
Supported by:
The National Natural Science Foundation of China (Grant No. 21272017 and 21572010).

摘要/Abstract

摘要：

多药耐药相关蛋白(Multidrug resistance-associated proteins, MRPs)是在人体广泛分布的一类跨膜转运蛋白。MRPs可选择性和特异性地将不同结构的药物、药物结合物及代谢物和其他的小分子化合物转到细胞外,使临床应用的抗肿瘤药物产生耐药性。因此,准确预测MRPs可转运的特异性底物分子特征,对于抗肿瘤药物的抗耐药性研发具有重要意义。本文利用MRPs的7个亚型(包括MRP1, -2, -3, -4, -5, -6和-8)已知的底物分子和MRPs整个家族共同转运的底物分子,应用CATALYST软件,基于分子共同特征分别构建了MRPs各亚型和整个家族的药效团模型。并利用DUD-E生成诱饵分子用于验证和选取药效团模型,选择AUC(area under curve)打分最好的药效团模型对人体内源性代谢数据库(HMDB)进行筛选,筛选获得的多个分子得到了文献的验证。通过对药效团筛选出的分子和已知的底物分子进行物理性质(ALOGP、分子极性表面、分子体积、分子质量、氢键受体数和氢键给体数)和结构骨架的比较分析,发现两者: 1)两者在ALOGP、分子体积、分子质量上具有一致的分布趋势; 2)与其他亚型相比, MRP1的底物分子具有较高的脂溶性,这与MRP1药效团模型中具有两个疏水中心相一致; 3)两者在骨架特征上,具有相同的骨架结构或相似的骨架片段。

Abstract:

Multidrug resistance-associated proteins (MRPs) can efflux structurally diverse drugs, drug conjugates, drug metabolites, as well as other small molecules out of the cells, and this is the main cause of producing multidrug resistance (MDR) of some anticancer drugs. Therefore, it is crucial to uncover the molecular features of MRPs substrates in developing anti-MDR cancer therapy.In the present study, common feature pharmacophore models were developed by employing CATALYST Pharmacophore Modeling and Analysis tools using substrates of MRPs, including MRP1, -2, -3, -4, -5, -6, -8 and MRPs family, respectively. The models were validated using independent decoy sets generated in DUD-E, and the ones with best AUC (area under the curve) scores were chosen to predict endogenous substrates by screening the Human Metabolome Database (HMDB). A number of molecules obtained by pharmacophore screening have been validated in the literatures. By comparing physical properties (ALOGP, Molecular_PolarSurfaceArea, Molecular_Volume, Molecular_Weight, Num_H_Acceptors, Num_H_Donors) and scaffold features of the screened candidates with the known substrates, we found that: 1) The two sets have consistent ALOGP, Molecule_Volume and Molecule_Weight distribution trend; 2) Substrates of MRP1 have a better lipophilicity than the other subtypes, which is consistent with the two hydrophobic centers on the MRP1 pharmacophore; 3) In the aspect of the scaffold structures, they have the identical or similar backbone fragments.

Key words: Multidrug resistance-associated proteins, Pharmacophore, Endogenous substrates, CATALYST, Decoys validation

中图分类号:

R916

Supporting:

Table S1. Training set substrates for HipHop hypotheses generation (numbers below structures represent ChEMBL ID)

Name	Training set substrates structures
MRP1
MRP2
MRP3
MRP4
MRP5
MRP6
MRP8
MRPs

Table S2. Top ranked hypotheses of MRP1-6, MRP8 and MRPs pharmacophore

Name	Hypotheses rank	Features	Scores
MRP1	hypo1	HHAA	49.533
	hypo2	HHAA	48.682
	hypo3	HHAA	48.605
	hypo4	HHAA	48.323
	hypo5	HHAA	48.323
	hypo6	HHAA	47.872
	hypo7	HHAA	47.714
	hypo8	HHAA	47.264
	hypo9	HHAA	47.11
	hypo10	HHAA	46.968
MRP2	hypo1	AAA	61.918
	hypo2	AAA	61.918
	hypo3	AAA	61.346
	hypo4	AAA	61.01
	hypo5	AAA	60.962
	hypo6	AAA	60.767
	hypo7	AAA	60.706
	hypo8	AAA	60.493
	hypo9	AAA	60.294
	hypo10	AAA	59.89
MRP3	hypo1	DAA	65.895
	hypo2	DAA	65.273
	hypo3	DAA	65.244
	hypo4	DAA	65.175
	hypo5	AAA	65.175
	hypo6	DAA	65.019
	hypo7	AAA	64.668
	hypo8	AAA	64.497
	hypo9	DAA	64.445
	hypo10	AAA	64.4
MRP4	hypo1	NDA	50.69
	hypo2	NDA	50.67
	hypo3	NDA	50.375
	hypo4	NAA	49.335
	hypo5	NDA	49.33
	hypo6	NDA	49.266
	hypo7	NDA	49.259
	hypo8	NDA	49.255
	hypo9	NDA	49.189
	hypo10	NDA	49.138
MRP5	hypo1	NAAA	61.686
	hypo2	NAAA	61.457
	hypo3	NAAA	61.044
	hypo4	NAAA	60.857
	hypo5	NAAA	60.715
	hypo6	NAAA	60.372
	hypo7	NAAA	60.249
	hypo8	NAAA	59.785
	hypo9	NAAA	59.37
	hypo10	NAAA	59.304
MRP6	hypo1	AAAAA	85.327
	hypo2	AAAAA	85.288
	hypo3	AAAAA	84.432
	hypo4	AAAAA	84.426
	hypo5	AAAAA	84.419
	hypo6	AAAAA	84.361
	hypo7	AAAAA	84.274
	hypo8	AAAAA	84.17
	hypo9	AAAAA	84.159
	hypo10	AAAAA	84.046
MRP8	hypo1	NAA	51.598
	hypo2	NAA	51.359
	hypo3	NAA	50.389
	hypo4	NAA	50.314
	hypo5	NAA	49.878
	hypo6	NAA	49.47
	hypo7	NAA	49.409
	hypo8	NAA	49.409
	hypo9	NAA	49.465
	hypo10	NAA	49.089
MRPs	hypo1	HHAAA	91.794
	hypo2	HHAAA	91.794
	hypo3	HHAAA	91.756
	hypo4	HHAAA	91.671
	hypo5	HHAAA	91.516
	hypo6	HHAAA	91.458
	hypo7	HHAAA	91.182
	hypo8	HHAAA	91.182
	hypo9	HHAAA	91.182
	hypo10	HHAAA	91.173

Summary of the features definitions

A: HBA, hydrogen bond acceptor; H: hydrophobic; D: HBD, hydrogen bond donor; N: neg_ionizable.

刘园, 陈亚, 胡建星, 刘振明, 张亮仁. 基于药效团模型的人多药耐药相关蛋白内源性底物的预测[J]. 中国药学(英文版), 2017, 26(8): 545-555.

Yuan Liu, Ya Chen, Jianxing Hu, Zhenming Liu, Liangren Zhang. In silico pharmacophore models to predict endogenous substrates for human multidrug resistance-associated proteins[J]. Journal of Chinese Pharmaceutical Sciences, 2017, 26(8): 545-555.

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基于药效团模型的人多药耐药相关蛋白内源性底物的预测

In silico pharmacophore models to predict endogenous substrates for human multidrug resistance-associated proteins

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