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基于氨基酸描述符对血管紧张素转化酶抑制五肽定量构效关系分析OA北大核心CSTPCD

Quantitative Structure-Activity Relationship Analysis of Angiotensin-Converting Enzyme Inhibitory Pentapeptides Based on Amino Acid Descriptors

中文摘要英文摘要

本研究收集近些年报道的血管紧张素转化酶(angiotensin converting enzyme,ACE)抑制五肽的氨基酸序列及抑制活性(半抑制浓度(half maximal inhibitory concentration,IC50)),建立ACE抑制五肽肽库,并将其氨基酸残基用Z-scales、VHSE和SVHEHS氨基酸描述符分别进行表述,以氨基酸残基的疏水性、立体性以及电性参数作为自变量,ACE抑制五肽的lg IC50作为因变量,利用Matlab软件用偏最小二乘法建立ACE抑制肽定量构效关系(quantitative structure-activity relationship,QSAR)模型,结果发现基于Z-scales描述符构建的ACE抑制五肽QSAR模型R2为0.641 1,Q2为0.536 9,模型预测五肽Gln-Arg-Pro-Asn-Met具有较高的ACE抑制活性,预测IC50为0.051 7 μmol/L,实测IC50为(0.040 0±0.008 3)μmol/L,预测值与实测值误差为0.011 7 μmol/L;基于VHSE描述符构建的QSAR模型R2为0.763 6,Q2为0.508 1,模型预测五肽Leu-Arg-Ala-Phe-Gln具有较高的ACE抑制活性,预测IC50为0.043 8 μmol/L,实测值为(0.027 3±0.005 3)μmol/L,误差为0.016 5 μmol/L;基于SVHEHS描述符构建的QSAR模型R2为0.840 5,Q2为0.400 5 μmol/L,模型预测五肽Tyr-Phe-Pro-Phe-Gln具有较高的ACE抑制活性,预测值为0.005 5 μmol/L,实测值为(0.0312±0.004 2)µmol/L,误差为0.025 7 µmol/L.3种建模方法对比发现,基于SVHEHS描述符构建的模型拟合能力最强,但是其预测能力较弱,而Z-scales和VHSE描述符所建模型能很好地对五肽进行QSAR分析,通过模型分析发现ACE抑制肽活性与其氨基酸的疏水特征呈负相关,与立体特征呈正相关.将3种ACE抑制肽与ACE蛋白(2X8J)进行分子对接,结果表明3种ACE抑制肽均可与ACE蛋白进行结合.本研究可为开发ACE抑制肽药物提供新手段,为开发和利用食源性ACE抑制肽提供理论基础.

This study aimed to investigate the structure-function relationship of angiotensin-converting enzyme(ACE)inhibitory peptides and to elucidate the action mechanism of food-derived ACE inhibitory peptides.Based on the amino acid sequences of recently reported ACE inhibitory pentapeptides and their half-maximal inhibitory concentration(IC50)values,a library of ACE inhibitory pentapeptides was generated and the structures of the ACE inhibitory pentapeptides were characterized using three amino acid descriptors,Z-scales,VHSE and SVHEHS.A partial least square(PLS)model for describing the quantitative structure-activity relationship(QSAR)of the ACE inhibitory peptides with the hydrophobic properties,steric properties,and electrical properties of amino acids as the independent variables and the lg IC50 of the ACE inhibitory pentapeptides as the dependent variable was established using Matlab software.The results showed that the R2 and Q2 of the QSAR model based on Z-scales descriptor were 0.641 1 and 0.536 9,respectively,and Gln-Arg-Pro-Asn-Met showed higher ACE inhibitory activity as predicted by this model.The predicted and measured IC50 were 0.051 7 and(0.040 0±0.008 3)μmol/L,respectively,and the error between them was 0.011 7 μmol/L.The R2 and Q2 of the QSAR model based on VHSE descriptor were 0.763 6 and 0.508 1,respectively,and Leu-Arg-Ala-Phe-Gln exhibited better ACE inhibitory activity as predicted by this model.The predicted and measured IC50 were 0.043 8 and(0.027 3±0.005 3)μmol/L,respectively,and the error between them was 0.016 5 μmol/L.The R2 and Q2 of the QSAR model based on SVHEHS descriptor were 0.840 5 and 0.400 5,respectively,and Leu-Arg-Ala-Phe-Gln displayed better ACE inhibitory activity as predicted by this model.The predicted and measured IC50 were 0.005 5 and(0.031 2±0.004 2)μmol/L,and the error between them was 0.025 7 μmol/L.Among the three QSAR models,the one based on SVHEHS descriptor had the strongest fitting capability but weak predictive capacity,while the models based on Z-scales and VHSE descriptors could allow good QSAR analysis of the pentapeptides.Our modeling analysis showed that the activity of the ACE inhibitory peptides was negatively correlated with the hydrophobic characteristics of amino acids and positively correlated with the steric characteristics of amino acids.Molecular docking of three ACE inhibitory peptides to ACE protein(2X8J)showed that all the ACE inhibitory peptides could bind to ACE protein.This study provides a new tool for developing ACE inhibitory peptides and a theoretical basis for the development and application of food-derived ACE inhibitory peptides.

郭星晨;李玉豪;马金璞;张钰璇;李华鑫;杨具田;樊佩如;高丹丹

西北民族大学生物医学研究中心,中国-马来西亚国家联合实验室,甘肃兰州 730030||西北民族大学生命科学与工程学院,甘肃兰州 730124西北民族大学生命科学与工程学院,甘肃兰州 730124四川大学化学学院,四川成都 610207

轻工业

血管紧张素转化酶偏最小二乘定量构效关系氨基酸描述符

angiotensin-converting enzymepeptidespartial least squaresquantitative structure-activity relationshipamino acid structure descriptors

《食品科学》 2024 (013)

38-48 / 11

西北民族大学中央高校基本科研业务费资金资助项目(31920230153);兰州市城关区科技计划项目(2022JSCX0011);甘肃省教育厅青年博士基金项目(2023QB-001);甘肃省科技厅技术创新引导计划-科技专员专项(23CXGA0078);甘肃省科技厅技术创新引导计划-科技型中小企业创新基金项目(23CXGP0002);甘肃省重点人才项目;西北民族大学校地合作项目配套基金资助项目(BELTY201901;HLRP-KY-20210902);国家自然科学基金地区科学基金项目(31960461);西北民族大学校级大学生创新创业训练计划项目(X202310742278);甘肃省高等教育教学培育项目(2022GSJXCGI09);西北民族大学校级创新创业教育示范专业项目(2022XJCXCYSFZY01)

10.7506/spkx1002-6630-20230613-106

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