ACE抑制肽定量构效关系研究

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摘要

近年来高血压发病率呈上升趋势，造成了沉重的家庭和社会负担。食源性血

管紧张素转化酶（angiotensin I-converting enzyme,ACE）抑制肽可通过抑制生物体

内ACE 活性起到良好的降压效果，但具体抑制机制尚不明确。定量构效关系

（quantitative structure activity relationship,QSAR）研究能提供分子结构与活性的定

量模型，对 ACE 抑制肽进行 QSAR 研究可为深入探讨 ACE 抑制肽的作用机制，

以及设计和开发高效降血压药物提供指导作用。

利用 20 种天然氨基酸的 457 种物化参数构建了新的氨基酸结构描述符

SVHEHS，并运用两个经典肽库对其效果进行评价。58 个ACE 抑制二肽的多元线

性回归（multivariate linear regression,MLR）模型相关系数（R2）为 0.936，均方根

误差（RMSE）为 0.259，交叉验证相关系数（Q2LOO）为 0.854，外部验证相关系

数（Q2ext）为 0.737；

48 个苦味二肽的 MLR 模型 R2为0.949，

RMSE 为0.136，

Q2LOO

为0.886，Q2ext 为0.543。与其他氨基酸描述符相比，SVHEHS 描述符建立的模型

在拟合能力、稳健性及预测能力上都更优，说明该描述符可用于对生物活性肽进

行定量构效关系研究。

利用 SVHEHS 描述符和 MLR 分别对自组建 ACE 抑制二、三、四肽进行 QSAR

研究。结果表明，二肽模型 R2=0.851，RMSE=0.327，Q2LOO=0.781，Q2ext=0.792，

且C端氨基酸残基疏水性质及电荷性质和 N端氨基酸残基立体特征对 ACE 抑制二

肽的活性影响较大，特别是 C端氨基酸残基强的疏水性和弱的电荷性质对其活性

有积极作用；三肽模型 R2=0.805，RMSE=0.339，Q2LOO=0.717，Q2ext=0.817，N端

第一位氨基酸残基的疏水性、电性特征、立体特征及 C端氨基酸残基的疏水性、

电性特征对肽的活性影响较大，尤以第一位氨基酸残基疏水性影响较为显著；四

肽模型 R2=0.792，RMSE=0.393，Q2LOO=0.553，Q2ext=0.630，四肽从 N端开始第三

位氨基酸残基的氢键贡献和电性特征对 ACE 抑制四肽的活性影响较大，尤以第三

位氨基酸残基氢键贡献与活性呈显著负相关；对三种肽合集的建模结果为：

R2=0.744，RMSE=0.508，Q2LOO=0.532，Q2ext=0.567。

利用 SVHEHS 描述符和偏最小二乘（partial least square regression,PLS）对自

组建 ACE 抑制二、三、四肽进行 QSAR 研究。结果表明，二肽模型 R2=0.607，

RMSE=0.587，Q2LOO=0.507，Q2ext=0.783，C端氨基酸残基的疏水性、电性特征、

立体特征和 N端氨基酸残基的立体特征与肽活性相关性较大；三肽模型 R2=0.852，

RMSE=0.232，Q2LOO=0.813，Q2ext=0.839，N端氨基酸残基的疏水性、立体特征和

C端氨基酸残基的立体、电性特征对活性有显著影响；四肽模型 R2=1，RMSE=0，

Q2LOO=1，Q2ext=0.935，从 N端开始第三位氨基酸残基的电性，第二位氨基酸残基

的立体特征，C端氨基酸残基的立体特征和 N端氨基酸的氢键贡献对肽活性影响

较大；对肽合集的建模结果为 R2=0.862，RMSE=0.356，Q2LOO=0.829，Q2ext=0.632。

利用 SVHEHS 描述符和人工神经网络（artificial neural networks,ANN）对自组

建ACE 抑制二、三、四肽进行 QSAR 研究。结果表明，二肽模型 R2=0.946，

RMSE=0.249，Q2LOO=0.951，Q2ext=0.852，N、C端氨基酸残基立体特征对肽活性

影响显著；三肽模型 R2=0.973，RMSE=0.135，Q2LOO=0.945，Q2ext=0.813，影响三

肽活性的主要因素为 N端氨基酸残基疏水性、立体特征和 C端氨基酸残基立体特

征等；四肽模型 R2=0.915，RMSE=0.250，Q2LOO=0.879，Q2ext=0.814，影响四肽活

性的主要因素为 C端氨基酸残基立体特征、第二位氨基酸残基电性特征以及第三

位氨基酸残基电性特征等；对肽合集的建模结果为：R2=0.958，RMSE=0.224，

Q2LOO=0.948，Q2ext=0.634。

最后，本文对 QSAR 建模的 3种算法优劣进行了比较，结果表明 MLR 能得到

明确的函数表达式，有利于解释和分析模型，但对变量正交性要求较严；PLS 对

小样本容量、多自变量个数且变量之间严重多重相关的肽库有很大优势，但拟合

能力稍差；ANN 能拟合非线性关系，快速逼近最佳样本数据规律，但无法获得明

确的函数关系式，且网络隐含层数和神经元节点个数没有确定规则，网络的学习

和记忆欠稳定。尽管 3种算法对自建肽库拟合能力有差异，但模型均能给出影响

肽活性的共性结构特征。影响二肽活性的结构主要是 C端氨基酸残基的疏水性和

N端氨基酸残基的立体特征；影响三肽活性的结构主要是 N端氨基酸残基的疏水

性和立体特征；影响四肽活性的结构主要是第三位氨基酸的电性特征。将不同长

度肽融合在一个模型，能减少建模时间，为预测更多不同长度的肽活性提供便利。

关键词：血管紧张素转化酶抑制肽定量构效关系氨基酸描述符主成

分分析多元线性回归偏最小二乘人工神经网络

ABSTRACT

In recent years the incidence of hypertension has been rising, and it has resulted in

heavy burden on society and family. Angiotensin I-converting enzyme(ACE) inhibitory

peptide from food protein can inhibit ACE activity in vivo significantly,but the specific

mechanism is unclear.Quantitative structure activity relationship(QSAR) research is

useful to establish quantitative model between the molecular structure and activity.

QSAR research on ACE inhibitory peptide is helpful for looking into action mechanism

of ACE inhibitory peptide,and then guiding the design and development of effective

hypotensor.

SVHEHS descriptor was derived from 457 physicochemical properties indexes of

20 natural amino acids,and its performance was tested by 58 angiotensin I-converting

enzyme inhibitory dipeptides and 48 bitter taste dipeptides.The correlative coefficient

R2,root mean square error RMSE,the cross-validation correlative coefficient Q2LOO and

the external validation correlation coefficient Q2ext of two models were 0.936, 0.259,

0.854, 0.737; 0.949, 0.136, 0.886 and 0.543,respectively.Compared the above statistics

parameters of models with other models constructed by various descriptors,the fitting

ability,robustness and forecasting ability of SVHEHS were better.These showed that the

descriptor can be used for QSAR research.

SVHEHS descriptor and multiple linear regression algorithm were used to study

the QSAR of angiotensin I-converting enzyme inhibitory dipeptides,tripeptides and

tetrapeptides.The parameters R2, RMSE, Q2LOO, Q2ext of dipeptides model were 0.851,

0.327, 0.781 and 0.792,respectively.Hydrophobicity and charge of C-terminal amino

acid,and steric properties of N-terminal amino acid were closely related with its activity.

Hydrophobicity of C-terminal amino acid was most positively associated with the

activity,and charge of C-terminal amino acid most negatively with the activity.The

parameters R2, RMSE, Q2LOO, Q2ext of tripeptides model were 0.805, 0.339, 0.717 and

0.817,respectively.Hydrophobic,electronic and steric properties of N-terminal amino

acid,hydrophobic and electronic properties of C-terminal amino acid were closely

related with its activity,especially the hydrophobicity of N-terminal amino acid was

significant factor with positive effect.The parameters R2, RMSE, Q2LOO, Q2ext of

tetrapeptides model were 0.792, 0.393, 0.553 and 0.630,respectively.Hydrogen bonds

contributions and electrical characteristics of the third amino acid were closely related

with its activity,especially hydrogen bonds contributions was significant factor with

negative effect.The parameters R2, RMSE, Q2LOO, Q2ext of all of the peptides model were

0.744, 0.508, 0.532 and 0.567,respectively.

SVHEHS descriptor and partial least square algorithm were used to study the

QSAR of angiotensin I-converting enzyme inhibitory dipeptides, tripeptides and

tetrapeptides.The parameters R2, RMSE, Q2LOO, Q2ext of dipeptides model were 0.607,

0.587, 0.507 and 0.783,respectively.Hydrophobic,electronic and steric properties of

C-terminal amino acid,steric features of N-terminal amino acid were closely related

with its activity.The parameters R2, RMSE, Q2LOO, Q2ext of tripeptides model were 0.852,

0.232, 0.813 and 0.839,respectively.Hydrophobic and steric properties of N-terminal

amino acid, electronic and steric properties of C-terminal amino acid were closely

related with its activity.The parameters R2, RMSE, Q2LOO, Q2ext of tetrapeptides model

were 1, 0, 1 and 0.935,respectively.Electronic properties of the third amino acid,steric

properties of the second amino acid,steric properties of the C-terminal amino

acid,hydrogen bonds contributions of N-terminal amino acid were closely related with

its activity.The parameters R2, RMSE, Q2LOO, Q2ext of all of the peptides model were

0.862, 0.356, 0.829 and 0.632,respectively.

SVHEHS descriptor and artificial neural networks algorithm were used to study

the QSAR of angiotensin I-converting enzyme inhibitory dipeptides,tripeptides and

tetrapeptides.The parameters R2, RMSE, Q2LOO, Q2ext of dipeptides model were 0.946,

0.249, 0.951 and 0.852,respectively.Steric features of N-terminal amino acid and

C-terminal amino acid were closely related with its activity.The parameters R2, RMSE,

Q2LOO, Q2ext of tripeptides model were 0.973, 0.135, 0.945 and 0.813, respectively.

Hydrophobic and steric properties of N-terminal amino acid, steric properties of

C-terminal amino acid were closely related with its activity. The parameters R2, RMSE,

Q2LOO, Q2ext of tetrapeptides model were 0.915, 0.250, 0.879 and 0.814, respectively.

Steric features of C-terminal amino acid,electronic properties of the second amino acid,

electronic properties of the third amino acid were closely related with its activity. The

parameters R2, RMSE, Q2LOO, Q2ext of all of the peptides model were 0.958, 0.224,

0.948 and 0.634, respectively.

Finally,advantages and disadvantages of the three algorithms were compared.

Multiple linear regression can obtained clear function expressions, and be helpful for

model interpretation and analysis. However, its requirements of variable orthogonality

was strict. Partial least squares had great advantage to small samples with many

independent variables and serious related data, but it could not fit all of the database

well. Artificial neural network can fit nonlinear relation, and approach the best sample

data law quickly, but it had no explicit function equation. Moreover, there were

uncertain rules for the network layers and nodes, learning and memory of the network

were less stable than the former two algorithms. Although fitting abilities of three

algorithms to peptide library varied from each other, models drew the common structure

features which affect the peptides activities. Hydrophobicity and charge of C-terminal

amino acid,steric properties of N-terminal amino acid were more associated with

dipeptides activity. Hydrophobic and steric properties of N-terminal amino acid were

associated with tripeptides activity much. Charge of the third amino acid was associated

with tetrapeptides activity much.Models, which established by peptides with different

length, can make a prediction for the activities of different peptides in one model. Hence,

it can consume less time for modeling. This method can facilitate activities prediction of

different peptides when the impact factors on activities were clear.

Key Words ：angiotensin I-converting enzyme inhibitory peptides,

quantitative structure activity relationship,amino acid descriptor,

principal component analysis, multiple linear regression,partial least

square regression,artificial neural networks

中文摘要

ABSTRACT

第一章绪论........................................................................................................1

§1.1 血管紧张素转化酶（ACE）与高血压.....................................................1

§1.1.1 ACE 概述 ............................................................................................1

§1.1.2 ACE 在血压调节中的作用..................................................................1

§1.2 ACE 抑制剂.................................................................................................2

§1.3 定量构效关系（QSAR）..........................................................................3

§1.3.1 二维定量构效关系(2D-QSAR).......................................................... 4

§1.3.2 三维定量构效关系(3D-QSAR).......................................................... 5

§1.3.3 QSAR 研究方法...................................................................................5

§1.4 ACE 抑制肽 QSAR 研究进展.................................................................... 8

§1.5 研究意义和内容.......................................................................................10

§1.5.1 研究意义............................................................................................10

§1.5.2 研究内容............................................................................................11

第二章氨基酸描述符 SVHEHS 的建立与效果评价...................................... 12

§2.1 原理和方法...............................................................................................12

§2.1.1 SVHEHS 的建立................................................................................ 12

§2.1.2 SVHEHS 的效果评价........................................................................ 14

§2.2 结果与分析...............................................................................................14

§2.2.1 经典 ACE 抑制二肽库用于 SVHEHS 的评价................................ 14

§2.2.2 经典苦味二肽库用于 SVHEHS 的评价.......................................... 17

§2.3 结论...........................................................................................................19

第三章 MLR 用于 ACE 抑制肽 QSAR 研究................................................... 21

§3.1 原理和方法...............................................................................................21

§3.1.1 ACE 抑制肽肽库的建立....................................................................21

§3.1.2 SVHEHS 描述符用于肽结构表征.................................................... 21

§3.1.3 MLR 建模与模型检验.......................................................................21

§3.2 结果与分析...............................................................................................22

§3.2.1 ACE 抑制二肽的 QSAR 研究........................................................... 22

§3.2.2 ACE 抑制三肽的 QSAR 研究........................................................... 24

§3.2.3 ACE 抑制四肽的 QSAR 研究........................................................... 28

§3.2.4 ACE 抑制肽库合集 QSAR 研究....................................................... 29

§3.3 结论...........................................................................................................34

第四章 PLS 用于 ACE 抑制肽 QSAR 研究..................................................... 36

§4.1 原理和方法...............................................................................................36

§4.1.1 ACE 抑制肽肽库的建立....................................................................36

§4.1.2 SVHEHS 描述符用于肽结构表征.................................................... 36

§4.1.3 PLS 建模与模型检验.........................................................................36

§4.2 结果与分析...............................................................................................36

§4.2.1 ACE 抑制二肽的 QSAR 研究........................................................... 36

§4.2.2 ACE 抑制三肽的 QSAR 研究........................................................... 40

§4.2.3 ACE 抑制四肽的 QSAR 研究........................................................... 45

§4.2.4 ACE 抑制肽库合集 QSAR 研究....................................................... 48

§4.3 结论...........................................................................................................52

第五章 ANN 用于 ACE 抑制肽 QSAR 研究................................................... 53

§5.1 原理和方法...............................................................................................53

§5.1.1 ACE 抑制肽肽库的建立....................................................................53

§5.1.2 SVHEHS 描述符用于肽结构表征.................................................... 53

§5.1.3 ANN 建模与模型检验....................................................................... 53

§5.2 结果与分析...............................................................................................54

§5.2.1 ACE 抑制二肽的 QSAR 研究........................................................... 54

§5.2.2 ACE 抑制三肽的 QSAR 研究........................................................... 56

§5.2.3 ACE 抑制四肽的 QSAR 研究........................................................... 59

§5.2.4 ACE 抑制肽库合集 QSAR 研究....................................................... 60

§5.3 结论...........................................................................................................64

第六章结论与展望............................................................................................65

§6.1 结论...........................................................................................................65

§6.2 展望...........................................................................................................67

参考文献..............................................................................................................68

在读期间公开发表的论文和承担科研项目及取得成果..................................76

致谢....................................................................................................................77

第一章绪论

高血压是指在静息状态下动脉舒张压（低压）≥90mmHg，收缩压（高压）≥

140mmHg。常伴有糖和脂肪代谢紊乱及脑、心、肾、视网膜等器官功能性或器质

性改变，是导致脑血管病、心脏病及肾脏病发生和死亡的最主要危险因素[1]。2000

年，全球约有26.4%的成年高血压病患者，据估计，到2025年全球将有29.2%的成

年高血压病患，即全球高血压病患人数将会超过15亿。我国估计现在有高血压病

患2亿，居民的高血压患病率还在不断升高，每年新增的高血压病人达1000万[2]。

我国高血压普遍存在着“三高”：患病率高、残疾率高和死亡率高，“三低”：知晓

率低、治疗率低和控制率低的特点[3]。研究发现高血压能导致期望寿命下降0.36岁，

而平均每个高血压病人死亡能导致11.4年的“早死”。高血压造成的经济负担包括

直接疾病经济损失、间接疾病经济损失以及无形经济损失。2003年我国年龄为

35~74岁的高血压患者直接经济负担达到201.5亿元[4]。

§1.1 血管紧张素转化酶（ACE）与高血压

§1.1.1 ACE 概述

ACE 是含 Zn2+的二肽羧肽酶，能被 Zn2+和Cl-激活，底物特异性较宽[5-6]，相

对分子量为 12000～15000，含 1306 个氨基酸残基，沉降系数为 7.9s，等电点 4.75。

研究发现，作为一种膜结合酶的 ACE，含有两个相对独立的同源程度较高的 C和

N结构域以及一个活性中心，且活性中心存在一个 Zn2+、疏水空腔和质子化的精

氨酸。 ACE 主要有睾丸状型和体细胞型两种亚型，两种亚型均由同一条基因转录

而来，但有不同的转录起始位置[7-8]。ACE 广泛存在于哺乳动物体内[9]，并以膜胞

外酶形式存在血管内皮细胞，其它种类的细胞中也有分布，如雄性幼芽细胞、吸

收性上皮细胞和神经上皮细胞。从组成结构上讲，ACE 也是一种含单一肽链的糖

蛋白，含大量的糖成分（以半乳糖、甘露糖和 N-乙酰基葡萄糖含量最多，占到总

量的 8～32%）。

§1.1.2 ACE 在血压调节中的作用

在体内升压调节系统肾素-血管紧张素系统（Renin-Angiotensin System,RAS）

和降压调节系统激肽释放酶-激肽系统（Kallikrein-Kinin System,KKS）中，ACE 扮

演着重要的角色（图 1-1）。RAS 是重要的调节血压内分泌系统，系统中的肾素能

将血管紧张素原 Asp-Arg-Val-Tyr-Ile-His-Pro-Phe-His-Leu-Val-Tyr-Ser-R 水解并释放

出血管紧张素 I（Ang I）Asp-Arg-Val-Tyr-Ile-His-Pro-Phe-His-Leu，没有活性的血

管紧张素Ⅰ，经 ACE 的作用后，脱掉 C末端二肽 His-Leu，转化成血管紧张素Ⅱ

（AngII）Asp-Arg-Val-Tyr-Ile-His-Pro-Phe。AngII 作为 RAS 中最主要生物活性成

分和最强的血管收缩剂，可以使外周小动脉的血管平滑肌强烈收缩以及心脏搏动

ACE 抑制肽定量构效关系研究

加速，迅速造成血压上升；另外它能刺激醛固酮分泌，直接作用于肠胃（减少肾

的血流量和促进人体对 Na+、K+的吸收），增加钠贮量和血容量，使血压迅速升高。

在人体内源性降压系统（又称抗高压因子）KKS 中，无活性的激肽原经激肽释放

酶作用，产生具有血管舒张功能的缓激肽 Arg-Pro-Gly-Phe-Ser-Pro-Phe-Arg，它能

使毛细血管扩展，通透性增加，起到舒张血管的作用，从而让血压降低。ACE 能

水解缓激肽，失去 C末端两个氨基酸，从而降解成失活片段，使缓激肽的降压作

用削弱[10-12]。

在血压调节中，RAS 和KKS 是一对相互拮抗的体系，它们的平衡协调对血压

维持在正常水平有重要意义。ACE 在这两个系统中起重要作用，ACE 活力如果过

高会破坏升压和降压两个系统的平衡，较多的 Ang II 生成与舒缓激肽的减少会导

致血压升高。如果 ACE 的活性被抑制，高血压的防治就能取得一定成效。ACE

抑制剂可以竞争性地抑制 ACE 活性，削弱 R A S 的升压作用，同时增强 KKS 的

降压效果，达到降低血压的目的[13-14]。

图1-1 血管紧张素转化酶对血压的调节作用

Fig.1-1 Effect of Angiotensin I-converting enzyme on blood pressure adjustment

§1.2 ACE 抑制剂

ACE 抑制剂通过跟血管紧张素转化酶直接结合或者以与底物竞争的方式跟

ACE 结合，使 ACE 的作用丧失。其作用包括抑制循环血肾素-血管紧张素Ⅱ系统；

抑制组织和局部血管紧张素系统；降低终末神经元释放去甲肾上腺素；减低内皮

系统形成内皮素；增强缓激肽及扩血管的前列环素系统；减低醛固酮分泌，使水

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摘要：

摘要近年来高血压发病率呈上升趋势，造成了沉重的家庭和社会负担。食源性血管紧张素转化酶（angiotensinI-convertingenzyme,ACE）抑制肽可通过抑制生物体内ACE活性起到良好的降压效果，但具体抑制机制尚不明确。定量构效关系（quantitativestructureactivityrelationship,QSAR）研究能提供分子结构与活性的定量模型，对ACE抑制肽进行QSAR研究可为深入探讨ACE抑制肽的作用机制，以及设计和开发高效降血压药物提供指导作用。利用20种天然氨基酸的457种物化参数构建了新的氨基酸结构描述符SVHEHS，并运用两个经典肽库对其效果进行评价。5...

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