New Quantitative StructureâActivity Relationship Model for

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A new quantitative structure-activity relationship model for Angiotensinconverting enzyme inhibitory dipeptides based on integrated descriptors Baichuan Deng, Xiaojun Ni, Zhenya Zhai, Tianyue Tang, Chengquan Tan, Yijing Yan, Jinping Deng, and Yulong Yin J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b03367 • Publication Date (Web): 06 Oct 2017 Downloaded from http://pubs.acs.org on October 7, 2017

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Journal of Agricultural and Food Chemistry

A new quantitative structure-activity relationship model for angiotensin-converting enzyme inhibitory dipeptides based on integrated descriptors 1

Baichuan Deng,†,ǁ Xiaojun Ni,†,ǁ Zhenya Zhai,† Tianyue Tang,† Chengquan Tan,†

2

Yijing Yan,† Jinping Deng,*,† Yulong Yin*,†,‡

3

†

4

Guangdong, P.R. China

5

‡

6

Poultry Production, Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute

7

of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, Hunan, P.R. China

College of Animal Science, South China Agricultural University, Guangzhou, 510642,

National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and

8

1

ACS Paragon Plus Environment


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ABSTRACT: Angiotensin-converting enzyme (ACE) inhibitory peptides derived

10

from food proteins have been widely reported for hypertension treatment. In this

11

paper, a benchmark dataset containing 141 unique ACE inhibitory dipeptides was

12

constructed

13

relationships (QSAR) study was carried out to predict half-inhibitory concentration

14

(IC50) of ACE activity. 16 descriptors were tested and the model generated by

15

G-scale descriptor showed the best predictive performance with the coefficient of

16

determination (R²) and cross-validated R² (Q²) of 0.6692 and 0.6220, respectively.

17

For most other descriptors, R² were ranging from 0.52-0.68 and Q² were ranging

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from 0.48-0.61. A complex model combining all 16 descriptors was carried out and

19

variable selection was performed in order to further improve the prediction

20

performance. The quality of model using integrated descriptors (R2 0.7340±0.0038,

21

Q² 0.7151±0.0019) was better than that of G-scale. An in-depth study of variable

22

importance showed that the most correlated properties to ACE inhibitory activity

23

were hydrophobicity, steric and electronic properties and C-terminal amino acids

24

contribute more than N-terminal amino acids. Five novel predicted ACE-inhibitory

25

peptides were synthesized and their IC50 values were validated through in vitro

26

experiments. The results indicated that the constructed model could give a reliable

27

prediction of ACE-inhibitory activity of peptides and it may be useful in the design

28

of novel ACE-inhibitory peptides.

through

database

mining

and

quantitative

2


structure–activity

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KEYWORDS: ACE-inhibitory peptides, QSAR, Variable selection, Variable

30

importance, Amino acid descriptors

31

INTRODUCTION

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Nowadays, inhibitors of angiotensin-converting enzyme (ACE) have been

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considered as first-line therapy for hypertension.1 ACE is a zinc- and chloride-

34

dependent metallopeptidase (EC. 3.4.15.1)2 and plays a dual role in regulating

35

renin-angiotensin system (RAS) and kallikrein-kinin system (KKS). It catalyzes the

36

conversion of inactive angiotensin I (decapeptide) to generate strongly

37

vasoconstrictive angiotensin II (octapeptide) as well as inactivates the vasodilator

38

bradykinin.3 Therefore, ACE has become an appropriate target for antihypertensives.

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The inhibition of ACE would lead to the reduction of angiotensin II production and

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consequently the decrease of blood pressure.4 Various synthetic ACE inhibitors,

41

such as captopril, enalapril, ramipril and lisinopril, have been developed for the

42

clinical treatment of hypertension.5 However, synthetic ACE inhibitors inevitably

43

cause adverse side effects such as cough, allergic reactions, taste disturbances, and

44

skin rashes.6 Thus, numerous ACE-inhibitory peptides have been identified from

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hydrolytic products of food-derived proteins and could be used as a potent

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functional food additive and represent a healthier and natural alternative to

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ACE-inhibitory drugs. The origin of these peptides were from milk,7 porcine 3



48

skeletal muscle,8 bovine collagen,9 bovine blood,10 egg,11 soybean,12 rapeseed,13 oat

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(avena sativa),14 marine,15 etc.

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Among approaches for studying bioactive peptides, many ACE-inhibitory peptides

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have been discovered by the classical approach, involving peptides production,

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isolation, purification and identification.16 Then, these newly discovered

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ACE-inhibitory peptides will be collected and deposited in related databases. Based

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on databases or literatures, bioinformatic approach has become a more efficient and

55

economical tool for peptide research and discovery of new bioactive peptides when

56

compared with the classical approach.17 Particularly, quantitative structure–activity

57

relationship (QSAR) is a crucial tool for bioinformatic approach and plays an

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important role in the study of bioactive peptides. In recent years, a number of

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experimentally validated ACE-inhibitory peptides were used to build QSAR models

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and in certain cases to predict novel and potent ACE-inhibitory peptides.5, 18 Among

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these, di and tri-peptides were most frequently studied because they have excellent

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biological properties such that they can be intact absorbed into blood circulation and

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they are usually resistant to gastrointestinal proteolysis.19 A classical dataset of

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dipeptide sequences of 58 ACE inhibitors20 are often utilized to test effectiveness of

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diverse kinds of amino acid descriptors in QSAR studies. A database consisting of

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168 dipeptides, in which 95 sequences are unique, was constructed from published

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literatures to study the QSAR of ACE-inhibitory peptides.21 Besides, most of 4


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previously studies only use a single amino acid descriptor to construct QSAR model,

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which may result in the loss of descriptive information and neglecting of the

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connection between different descriptors.

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Databases such as BIOPEP,22 ACEpepDB (http://www.cftri.com/pepdb/index.php)

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and PepBank23 contain ACE-inhibitory peptides, but the number is limited. The

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records with experimentally validated IC50 values are even fewer. Recent years, new

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ACE-inhibitory peptides are continuously reported in literatures. Kumar et al.

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established a specific and new database for antihypertensive peptides, AHTPDB,

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which contains 5978 peptide entries.24 Among these, 3364 entries have provided

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information of IC50 values of peptides and 1694 were unique peptides.24 Moreover,

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this database contains 1878 records of dipeptides, including 141 unique dipeptides

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sequences with IC50 values.

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In this study, we used the 141 unique ACE-inhibitory dipeptides from AHTPDB to

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construct a dataset. It is, to our knowledge, the largest number of unique dipeptides

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ever used in a single QSAR model. 16 different descriptors were used to construct a

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sophisticated QSAR model in order to use more comprehensive information to

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describe amino acids. We also used outlier elimination and variable selection

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methods to optimize the model and improve the prediction performance. The newly

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predicted ACE-inhibitory peptides were synthesized and their IC50 values were

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validated through in vitro experiments. The objectives of this study were to 5



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construct a reliable QSAR model for ACE-inhibitory dipeptides prediction and it

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may be useful in the design of novel ACE-inhibitory peptides.

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MATERIALS AND METHODS

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Chemicals. Angiotensin-converting enzyme (ACE) from rabbit lung and

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hippuryl-histidyl-leucine (HHL) as a substrate of ACE were purchased from

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Sigma–Aldrich (St. Louis, MO, USA). The chemically synthesized (purity >95%)

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peptides were obtained from DGpeptidesCo., Ltd. (Hangzhou, China). All the other

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reagents used in this study were of analytical pure.

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Assay for ACE-inhibitory activity. ACE-inhibitory activity was assayed by the

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method of Cushman and Cheung (1971) with slight modifications.25 The peptide

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solution (50 µL) was mixed with 5 mM HHL solution (150 µL), followed by

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pre-incubation for 5 min at 37 ℃. Afterwards, 50 µL of a 25 mU/mL ACE solution

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(prepared in a 0.1 M sodium borate buffer containing 0.3 M NaCl at pH 8.3) was

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added, the reaction mixture was further incubated for 30min at 37 ℃. The enzymic

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reaction was terminated by adding 250 µl of 1 M HCl and the liberated hippuric acid

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was extracted with 1.5 ml of ethyl acetate by vortex mixing for 30 sec. After

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centrifugation (4000 × g, 10 min), 1 ml aliquot of the upper layer was transferred

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into a glass tube, and evaporated by heating at 120 ℃ for 30 min. The hippuric acid 6


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was redissolved in 3 ml of distilled water. The absorbance was measured at 228 nm

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using UV-spectrophotometer (UV-2501PC, Shimadzu, Tokyo, Japan). The IC50

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value was defined as the concentration of the inhibitor required to inhibit 50% of the

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ACE-inhibitory activity.

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Data collection. Datasets for the antihypertensive peptides (AHTPs) were manually

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collected from AHTPDB (http://crdd.osdd.net/raghava/ahtpdb/), which is a database

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of experimentally validated antihypertensive peptides and most of the peptides

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belong to the family of angiotensin I converting enzyme inhibiting peptides.24

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Source of information of this database were mainly collected from three databases,

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i.e.

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(http://www.uwm.edu.pl/biochemia/index.php/pl/biopep)

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database (http://erop.inbi.ras.ru/), and published literatures. First of all, we selected

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all the dipeptides in this database, including information about sequence, half

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maximal inhibitory concentration (IC50), IC50 determination assay, source and

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molecular mass. IC50 represents the concentration that inhibits 50% activity of ACE.

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Data processing. During data collection from the database, it was noticed that many

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of the identical peptides have exhibited the same or different IC50 values. For the

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peptide with multiple IC50 values, the median value was retained to remove

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duplicates. A total of 1878 dipeptides were obtained before merging. Then, the total

ACEpepDB

(http://www.cftri.com/pepdb/),

7


and

BIOPEP EROP-Moscow


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number of unique peptides included in QSAR model is 141. All the IC50 values were

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log-transformed prior to modeling.

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Building the QSAR model. QSAR is defined as a relationship linking structural

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characteristics of molecules to their biological or physicochemical properties. Data

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sets for the processed dipeptides are presented in Table S1. The peptide sequences

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were transformed into X-matrix by means of 16 descriptors, respectively, while

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dependent variable Y represents activity values (IC50) of peptides. These descriptors

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were collected from published articles which can well represent the structural

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characteristics of the amino acids for QSAR models, including Z-scale,26 5Z-scale,27

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DPPS,28 MS-WHIM,29 ISA-ECI,30 VHSE,31 FASGAI,32 VSW,33 T-scale,34 ST-scale,35

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E-scale,36 V,37 G-scale,38 HESH39 and HSEHPCSV.40 For a set of peptides analogues,

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the structures would be characterized by describing each varied amino acid position

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with the descriptor’s parameter values. For example, the G-scale descriptor

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including eight kinds of parameters, if we used it to describe dipeptides, the

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chemical structures of dipeptides would be described by 16 (8 parameters × 2 amino

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acids) variables. Thus, a set of peptide sequences varied in n positions can be

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described by 8×n variables. The amino acid at the N-terminus was designated as n1,

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and its properties were described as n1G1, n1G2, n1G3, n1G4, n1G5, n1G6, n1G7

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and n1G8 of the G-scale model. The C-terminus was designated as n2 and so on.

8


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After that, a combination of 204 variables for each dipeptides was undertaken, and

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204 predictor variables were defined with the above descriptors express as:

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Z-scale(D1-D6); 5Z-scale(D7-D16); DPPS(D17-D36); MS-WHIM1(D37-D42);

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MS-WHIM2(D43-D48);

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FASGAI(D69-D80);

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ST(D119-D134);

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HSEHPCSV(D181-D204).

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Partial least square (PLS) regression41 was used to build the correlation between

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amino acid descriptors (predictors, X) and log-transformed IC50 (dependent, Y) and

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it was implemented using MATLAB R2015a software. All variables were

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auto-scaled to unit variance prior to the analyses. The data set was validated by

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cross-validation as internal validation, the number of significant PLS components

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was chosen automatically by using various rules based on a statistic called Q²,

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which is the cross-validated R², referred to as the predictive ability of the model. R²

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is the coefficient of determination, which is also an important parameter in PLS

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analysis and provides an estimate of the model fit.

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Model population analysis (MPA). MPA is a general framework for chemical

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modeling which uses random resampling and statistical analysis techniques to

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extract important information from the data.42 Generally, it contains three steps: (1) a

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random resampling procedure to obtain sub-datasets, (2) a model building procedure

ISA-ECI(D49-D52); VSW(D81-D98);

V(D135-D140);

E(D99-D108);

G(D141-D156);

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VHSEA(D53-D68); T(D109-D118); HESH(D157-D180);


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to build sub-models, (3) and a statistical analysis procedure to extract information

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from the outcome of sub-models. In this study, MPA was applied for outlier

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detection and variable selection.

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Outlier detection. In an attempt to obtain a robust and highly predictive model, it is

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crucial to identify and remove outlying samples from measured data before

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modeling. The MPA-based method was used to detect outliers of the data.43 To begin

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with, a number of (e.g. 5000) sub-datasets were generated by applying random

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resampling procedure in sample space. Each sub-dataset contains 80% of random

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selected samples from the pool of samples. Then, for each sub-dataset, a PLS

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regression were built. Thus, a number of (e.g. 5000) were built. In the next step, the

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sub-models were used to predict the IC50 value of remaining samples separately and

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the prediction errors for each sample were recorded. Finally, for each sample, a

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statistical analysis was applied on the prediction errors. The average of prediction

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errors (MEAN) and standard deviation of prediction errors (STD) were used as the

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basis for outlier detection. In this study, 3-sigma rule was applied and the samples

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which exceed the range of mean±3*standard deviation for MEAN (or STD) were

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considered as outliers. This method eliminated outliers one by one until all samples

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were within the range.

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Variable selection. Variable selection was carried out after excluding the outliers. In 10


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the present study, a bootstrapping soft shrinkage (BOSS) method was applied for

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variable selection.44 It is also based on the idea of MPA.42 Firstly, a number of

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sub-models were generated using bootstrap resampling in sample space. Then, for

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each sub-model, the regression coefficients were extracted. The regression

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coefficients for sub-models were summed up to obtain weights for variables. In the

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next step, weighted bootstrap resampling45 was applied to build new sub-models,

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where variables with larger weights had larger probabilities to be selected into the

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sub-models. The resampling procedure was repeated and the less important variables

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were eliminated gradually. This variable selection method used multi-model instead

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of single model for comparison and considered random combination of variables,

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which had advantages in selecting optimal variable combination compared with

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previous methods.44 The selected variable is represented as n1/n2-descriptor’s

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name-the parameter number, where n1 denotes N-terminus and n2 denotes

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C-terminus. For example, ‘n1-G-1’ means that the selected important variable is the

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first parameter of the G-scale to describe the amino acid at N-terminus.

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Statistical Analysis. All statistical analyses were performed by using MATLAB

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software (Version R2015a, the Mathworks, Inc).

11



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RESULTS AND DISCUSSION

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(Please insert Table 1)

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QSAR study on ACE inhibitory dipeptides. Modeling of these dipeptides was

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conducted according to the data sets in the Table S1. Table 1 summarizes the most

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important statistical parameters of the model based on dipeptides dataset using the

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above 16 kinds of descriptors. After elimination of outliers, the final sizes of

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calibration dataset were slightly different and resulted in substantially improved

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models. According to the three-sigma rule, each descriptor excluded 2-6 outliers.

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Figure 1 shows the process of outlier elimination on the model built with G-scale

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descriptor. The outlier numbers were displayed in each figure of Figure 1a-e, the

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elimination order was 130, 80, 127 and 125, respectively. All samples were within

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the range according to the three-sigma rule (dashed line) after removing outliers

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(Figure 1e). The process of removing outliers for QSAR model with other

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descriptors is the same as G-scale descriptor. After eliminating outliers, all models

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established and descriptors were presented in Table 1. It can be seen clearly that the

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model derived from G-scale descriptor has the best predictive performance.

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Modeling of the G-scale descriptor with activities has the higher Q² (0.6220) and

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could explain 66.92% of the sum of squares in Y-variance (R2) after excluding

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outliers. The Q² value of G-scale, HSEHPCSV, 5Z-scale models are larger than 0.6.

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For most of the other descriptors, Q² values are between 0.5 and 0.6. Only the 12


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models of MS-WHTM1 and ISA-ECI show Q² values of lower than 0.5.

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(Please insert Figure 1)

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To further improvement the model, we built the model using integrated variables,

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where variables of 16 descriptors were combined. And a large dataset with 204

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variables were obtained. It was followed by outlier elimination and variable

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selection on PLS regression models. The aim of this process is to integrate the

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information of different descriptors together to make a better model. The process of

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outlier elimination on integrated model is displayed in Figure 2. The order of outlier

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elimination is 127, 130, 125, 124 and 80, respectively. All samples were within the

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range according to the three-sigma rule (dashed line) after getting rid of all outliers

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(Figure 2f). Table 1 shows that the Q²and R² values obtained by using integrated

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descriptors are 0.6205 and 0.7110, respectively. It is comparable to the result of the

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best performed single descriptor (G-scale descriptor). Moreover, the integrated

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model leaves room for further improvement of the model, superior to any

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single-descriptor models.

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Variable selection. The bootstrapping soft shrinkage (BOSS) method was applied

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for variable selection on the integrated descriptors model.44 The effective of BOSS

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has been proved elsewhere.46 In the present study, BOSS was run 100 times and the

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results were shown in Table 1. Compared the model with all descriptors, variable 13



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selection not only reduced the number of variables, but also improved the prediction

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performance of the model. The Q² values after variable selection is 0.7151. It has a

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distinct improvement compared to the full-variable model, of which the Q² value is

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0.6205 (Table 1). On average, 48 variables were selected from 204 variables by the

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variable selection method. Researches showed that not all molecular descriptors

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were related to biological activity, so it is necessary to delete redundant descriptors

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to improve the prediction performance of the QSAR model.39 Moreover, it also

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emphasizes the importance of removing outliers and variable selection method in

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QSAR modeling.

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Comparison with the reported models. QSAR studies have been carried out on 58

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ACE-inhibitory dipeptides using T-scale, G-scale and HESH descriptors, with the Q2

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values 0.784, 0.831 and 0.838, respectively (Table 2).38 The Q2 value of integrated

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descriptors combined with BOSS is 0.910, which is larger than previous reports. Wu

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et al. carried out QSAR study of 168 ACE inhibitor dipeptides with Z-scale with the

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Q² of 0.711 and R2 of 0.732.21 Fu et al. further improved the model to obtain Q2

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0.716 and R2 0.746.9 By using integrated descriptors the Q2 and R2 were further

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improved, which are 0.804 and 0.816, respectively (Table 2). The comparison of the

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results with the previous reports showed that our method can give higher prediction

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accuracy on the same datasets.

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It should be noted that the dipeptides in Wu’s study contained 72 duplicated 14


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sequences (only 94 unique dipeptides). The existence of duplicated sequences may

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result in an over-optimistic Q2. In the present study, duplicated sequences were

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eliminated and the median of IC50 value for a unique sequence was retained. As a

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result, 141 unique dipeptides were used for modeling. It is, to our knowledge, the

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largest number of unique dipeptides ever used in a single QSAR model. Thus, the

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prediction performance of our model (Q2=0.7151) is better or comparable with the

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previous studies.

268


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Evaluate the importance of variables. For the 16 single descriptor models, the

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importance of amino acid properties in each position is evaluated using the value of

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PLS regression coefficients and variable importance in project (VIP).47 Figure 3

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shows the evaluation of variable importance of G-scale model. Through the PLS

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regression coefficients values (Figure 3a), it is observed that variables of G1, G5, G6

274

and G7 are important for the bioactivities of ACE-inhibitor dipeptides. For the

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position n1, G1, G2, G4 and G5 are negatively related to the log values, while G3,

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G6, G7 and G8 are positively related to the log values. For the position n2, G1, G2,

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G3, G5 and G6 are negatively to the log values, while G4, G7 and G8 are positive. It

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is evident that position n2 is more relevant to biological activities than position n1.

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G-scale descriptor including eight kinds of parameters were derived from 457 kinds

280

of physicochemical properties of the amino acid index database, which was 15



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classified into three sorts of parameters including hydrophobic, steric and electric

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properties.38 The eight parameters were encoded as G1∼G8 which represented

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hydrophobicity, STERIMOL minimum width of the side chain, loss of side chain

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hydropathy by helix formation, optical rotation, side chain molecular volume,

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frequency of the 4th residue in turn, amino acid composition of EXT of

286

multi-spanning proteins and net charge index, respectively. For the ACE-inhibitory

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dipeptides, amino acid residues with information of hydrophobic and stereo

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characteristics are most important to biological activities. The importance of the

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amino acid residue in position n2 is mainly decided by G1 followed by G5, which

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represents hydrophobicity, side chain molecular volume, respectively. For both

291

positions, amino acid residues with large bulk chain as well as hydrophobic side

292

chains are preferred, such as phenylalanine, tryptophan, and tyrosine. VIP plots of

293

the PLS models using G-scale descriptor are summarized in Figure 3b. VIP reflects

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the size of the contribution for variables to activity. For the QSAR model using

295

G-scale descriptor, the most influential property parameters to ACE- inhibitory

296

dipeptides are G1, G5 and G7, and the properties contributing to the model are

297

Hydrophobicity > Steric Property > Electric Properties according to the VIP value. It

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is obvious that the position n2 is most influential to biological activities and the

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order of the important variables in n2 is G5 > G1 > G7 > G3 > G2 (VIP value >1).

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For position n1, eight parameters are arranged in a proper order as G2 > G1 > G8 >

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G5 > G7 > G6 > G3 > G4. According to these results, the results of PLS regression 16


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coefficients and VIP are similar.

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The variable importance evaluation of E-scale, HSEHPCSV, 5Z-scale, VHSEA and

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Z-scale showed that hydrophobicity and steric properties were important for the

305

bioactivities. For HESH, the significant properties contributing to the model was

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hydrophobicity, especially for the C-terminus. The regression coefficients of

307

FASGAI show that the vital parameters of the bulky properties may be conducive to

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enhancing bioactivities of ACE inhibitors. The properties of the important variables

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of DPPS were steric and electronic properties, while for 3D descriptors, relative

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importance of the X-variables of ISA-ECI in the QSAR model was isotropic surface

311

area, MS-WHIM descriptor was primarily of electrostatic potential. According to the

312

regression coefficients and VIP values of all the descriptors, it could be seen that

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position n2 (C-terminus) of the dipeptide played an important role in ACE-inhibitory

314

activity. For the important properties of variables, hydrophobicity, steric properties,

315

and electronic properties were crucial, in addition to hydrogen bonding.

316

There were many studies speculated that the amino acid with hydrophobic property

317

on C-terminus was positively highest correlated with ACE inhibitors bioactivity.18, 20,

318

31

Dipeptides with aromatic side chains and proline on C-terminus and branched

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aliphatic side amino acids on N-terminus were essential for high inhibitory

320

activity.48 Our results are in agreement with the previous findings.

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(Please insert Figure 4) 17



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The BOSS method includes some randomness in its algorithm, so that the selected

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variables in each run can be slightly different. This property gives BOSS a new way

324

of evaluating variable importance, i.e. the frequency of selected variables. The

325

variables which have higher frequency of being selected by BOSS show higher

326

variable importance. Figure 4 displayed the frequency of variables selected by

327

BOSS method on ACE dipeptide dataset. Based on BOSS method, after running 100

328

times, the frequency of the selected variables was shown. Among 16 descriptors,

329

G-scale and HSEHPCSV have the highest frequency, followed by ST-scale,

330

FASGAI, MS-WHIM and VSW. Back to the parameters of each descriptor, the top 8

331

variables with descending order are as follows: ST-4 > G-1 > FASGAI-6 >

332

HSEHPCSV-11 > G-3 > G-8> HSEHPCSV-4 > VSW-5 > MS-WHIM2-1. They may

333

have high correlated with ACE inhibitors bioactivities. In other words, properties of

334

hydrophobicity, steric, electronic and hydrogen bonding were more relevant to the

335

biological activities of ACE-inhibitor dipeptide.

336

It can be seen from Figure 3 that the variables with high frequency comes from

337

different descriptors, and the combination of descriptors greatly improved the

338

prediction ability of the QSAR model. Most of the highly selected descriptors, such

339

as G-scale, HSEHPCSV, FASGAI, have good performance when applied

340

separately in QSAR models. However, some descriptors, such as ST-scale,

341

MS-WHIM, have poor predictive performance when modeled separately. They also 18


Page 19 of 33


342

have high frequency when applied in variable selection. Coupled with the fact that

343

the model obtained by BOSS has improved prediction performance, we may

344

conclude that these descriptors are also important in QSAR model building. It is

345

suggested that BOSS can extract additional information from the poorly performed

346

descriptors and have considered the interaction with highly performed descriptors.

347


348

Prediction and validation of potential ACE-inhibitory dipeptides. According to

349

the constructed QSAR models, the ACE-inhibitory activities of the remaining

350

dipeptides were predicted. Inevitably, there is a certain degree of variation based on

351

QSAR models, therefore, in vitro experiments is required to further validate the

352

activity of peptides predicted.8 In this study, five predicted dipeptides, which had the

353

lowest predicted IC50, were synthesized chemically to determine the IC50 values.

354

Table 3 displays the comparison between predicted and experimental values of

355

dipeptides. The predicted logIC50 values of CW, TW, HW, QW and CY were 0.98,

356

1.20 1.24, 1.24 and 1.35, respectively. The experimental values were 0.54, 1.15, 1.09,

357

2.03 and 1.63, respectively. It can be seen that all these 5 predicted dipeptides are

358

verified to have ACE-inhibitory activities and all the prediction errors are lower than

359

0.5, except for QW. Among the five dipeptides, CW has the lowest predicted

360

logIC50 and measured logIC50, which means the highest ACE-inhibitory activity.

361

Besides, HW, TW, CY and QW also show strong ACE-inhibitory activities. Based 19



Page 20 of 33

362

on structure-activity relationship, it has been suggested that high ACE-inhibitory

363

activity of the peptides should have hydrophobic amino acids, especially on

364

C-terminus. The C-terminus of the five predicted dipeptides contains tryptophan or

365

tyrosine, showing strong hydrophobicity. These results indicated the validity of the

366

prediction models, which could provide a reliable prediction on ACE-inhibitory

367

activity of peptides.

368

In conclusion, we constructed a benchmark dataset for QSAR study of ACE

369

inhibitory dipeptides, which contains 141 unique dipeptides. It is, to our knowledge,

370

the largest number of unique dipeptides ever used in a single QSAR model. Among

371

the 16 amino acid descriptors, G-scale descriptor has the best predictive

372

performance and can be selected to describe the structure of ACE-inhibitory

373

dipeptides. Meanwhile, further improvement on the predictive ability of the QSAR

374

model was obtained using integrated descriptors combined with variable selection

375

method. The newly predicted ACE-inhibitory peptides were validated through in

376

vitro experiments, which verified the reliability of the model. The QSAR model we

377

built may be useful in the design of novel ACE-inhibitory peptides.

378

AUTHOR INFORMATION

379

Corresponding Authors

380 381

*

(J.D.) Fax: +86 84615285 Tel: +8613787136677 E-mail: [email protected]

*

(Y.Y.) Fax: +86 84615285 Tel: +8613974915255 E-mail: [email protected] 20


Page 21 of 33

382 383


Author Contributions ǁ

Baichuan Deng and Xiaojun Ni contributed equally.

384

Funding

385

The authors gratefully thank the National Natural Science Foundation of China for

386

support of the projects (Grant Nos. 31330075, 31572420 and 31110103909). The

387

studies meet with the approval of the university’s review board.

388

Notes

389

The authors declare no competing financial interest.

390

ABBREVIATIONS USED

391

ACE, angiotensin-converting enzyme; QSAR, quantitative structure-activity

392

relationship; PLS, Partial least square regression; IC50, half maximal inhibitory

393

concentration; VIP, variable importance in project; BOSS, bootstrapping soft

394

shrinkage method.

395

Supporting Information

396

A table listing the sequence and IC50 values of 141 unique ACE-inhibitory

397

dipeptides.

21



Page 22 of 33

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inhibitor oligopeptides based on a novel set of sequence information descriptors. J. Mol. Model. 2011, 17, 1599-1606. (39) Shu, M.; Mei, H.; Yang, S.; Liao, L.; Li, Z., Structural Parameter Characterization and Bioactivity Simulation Based on Peptide Sequence. QSAR Comb. Sci. 2009, 28, 27-35. (40) Dan-Qun; LIANG; Gui-Zhao; ZHANG; Zhi-Liang, New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship. Chin. J. Struct. Chem. 2008, 27, 1375-1383. (41) Wold, S.; Sjöström, M.; Eriksson, L., PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2008, 58, 109-130. (42) Deng, B. C.; Yun, Y. H.; Liang, Y. Z., Model population analysis in chemometrics. Chemometrics & Intelligent Laboratory Systems 2015, 149, 166–176. (43) Cao, D. S.; Liang, Y. Z.; Xu, Q. S.; Li, H. D.; Chen, X., A new strategy of outlier detection for QSAR/QSPR. Journal of Computational Chemistry 2010, 31, 592–602. (44) Deng, B. C.; Yun, Y. H.; Cao, D. S.; Yin, Y. L.; Wang, W. T.; Lu, H. M.; Luo, Q. Y.; Liang, Y. Z., A bootstrapping soft shrinkage approach for variable selection in chemical modeling. Anal. Chim. Acta 2016, 908, 63-74. (45) Barbe, P.; Bertail, P., The Weighted Bootstrap. Lecture Notes in Statistics 1995, 98. (46) Lin, Y. W.; Deng, B. C.; Wang, L. L.; Xu, Q. S.; Liu, L.; Liang, Y. Z., Fisher optimal subspace shrinkage for block variable selection with applications to NIR spectroscopic analysis. Chemometrics & Intelligent Laboratory Systems 2016, 159. (47) Wold, S.; Johansson, E.; Cocchi, M., PLS: Partial Least Squares Projections to Latent Structures, 3D QSAR in drug design. 1993; Vol. 1, p 523-550. (48) Cheung, H. S.; Wang, F. L.; Ondetti, M. A.; Sabo, E. F.; Cushman, D. W., Binding of peptide substrates and inhibitors of angiotensin-converting enzyme. Importance of the COOH-terminal dipeptide sequence. J. Biol. Chem. 1980, 255, 401-407.

24


Page 25 of 33


Table1. Comparisons among different QSAR models for ACE inhibitory dipeptidesa Before outlier elimination

After outlier elimination

Descriptors

Q2

R2

optPC

Q2

R2

optPC

Outlier

G

0.5331

0.5619

1

0.6220

0.6692

2

130, 80, 125,127

E

0.4589

0.5202

1

0.5965

0.6490

1

130, 80, 125,127,124

HSEHPCSV

0.4824

0.5920

4

0.6087

0.6809

3

80,127,130,125

5Z_scale

0.4132

0.5181

5

0.5629

0.6294

5

130, 125,127, 124,123

HESH

0.4306

0.4823

1

0.5419

0.6776

8

127, 130, 125,124

FASGAI

0.4521

0.5074

1

0.5918

0.6436

2

127, 130, 125, 80

VHSEA

0.4980

0.5396

1

0.5650

0.6033

1

127, 125, 130

V

0.4420

0.4715

1

0.5501

0.5827

2

127, 80, 125, 130

T

0.4943

0.5585

6

0.5716

0.6271

6

127, 124, 130, 125

ST

0.4918

0.5859

4

0.5755

0.6457

4

127, 130, 124

Z_scale

0.4753

0.5149

2

0.6028

0.6387

3

130,125, 127, 124,123, 80

DPPS

0.4704

0.5449

2

0.5501

0.6155

2

125, 127, 130

VSW

0.5170

0.6101

2

0.5430

0.6185

1

127, 125, 130

MS_WHTM2

0.4294

0.4782

2

0.5348

0.5808

5

127, 124,130, 125

MS_WHTM1

0.4069

0.4540

1

0.4809

0.5238

1

127, 125

ISA_ECI

0.4323

0.4709

4

0.4851

0.5239

4

130, 127

0.5095

0.5528

1

0.6205

0.7110

2

127, 125, 130, 124, 80

0.7151±

0.7340±

2±

0.0019

0.0038

0.4976

Integrated descriptors BOSS a

R² is the coefficient of determination; Q² is the cross-validated R²; optPC is optimal principal components for PLS regression model; the results of BOSS are shown in the form of mean value ± standard deviation in 100 runs. 502

25



Page 26 of 33

Table 2. The comparison between QSAR models of ACE-inhibitory dipeptides using reported datasets Dataset

Descriptor

Modeling Method

Q2(CV)

R2

Ref

58 dipeptides 58 dipeptides 58 dipeptides 58 dipeptides 168 dipeptides 168 dipeptides 168 dipeptides

T-scale G-scale HESH Integral + BOSS Z-score 5Z-scale Integral + BOSS

PLS PLS PLS PLS PLS PLS PLS

0.784 0.831 0.838 0.910±0.002 0.711 0.716 0.804±0.001

0.868 0.870 0.877 0.937±0.004 0.732 0.746 0.816±0.002

34

26


38 39

21 9

Page 27 of 33


Table3. Prediction and experimental validation of potent ACE-inhibitory dipeptidesa logIC50 peptides predicted observed error CW 0.98 0.54 -0.44 TW 1.20 1.15 -0.05 HW 1.24 1.09 -0.15 QW 1.24 2.03 0.79 CY 1.35 1.63 0.28 a Predicted activity refers to the values obtained from PLS regression model; observed activity refers to the experimentally determined activity using synthetic dipeptides; logIC50 refers to the logarithmic form of IC50 (µM). 503

27



Page 28 of 33

Figure captions

Figure 1. The process of outlier elimination on the model built with G-scale descriptor. The dashed line is defined as the threshold for outliers, which is mean±3*standard deviation for MEAN (or STD). (a) No outlier was eliminated, (b) sample no. 130 was eliminated, (c) sample no. 80 was eliminated, (d) sample no. 127 was eliminated, (e) sample no. 125 was eliminated and all outliers were removed from the model.

Figure 2. The process of outlier elimination on the model built with integrated descriptors. The dashed line is defined as the threshold for outliers, which is mean±3*standard deviation for MEAN (or STD). (a) No outlier was eliminated, (b) sample no. 127 was eliminated, (c) sample no. 130 was eliminated, (d) sample no. 125 was eliminated, (e) sample no. 124 was eliminated, (f) sample no. 80 was eliminated and all outliers were removed from the model.

Figure 3. (a) PLS regression coefficients and (b) VIP of the G-scale model of the ACE -inhibitory dipeptides. The larger value of VIP and the larger absolute value of regression coefficients denote higher variable importance.

Figure 4. The frequency of variables selected in BOSS method on ACE dataset (100 runs). The higher frequency denotes higher variable importance.

28


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Figure 1

29



Figure 2

30


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Figure 3 504

31



Figure 4 505

32


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Graphic for table of contents.

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