High-Throughput and Rapid Screening of Novel ACE Inhibitory

Oct 31, 2017 - ABSTRACT: Several novel peptides with high ACE-I inhibitory activity were successfully ... Overall, sericin protein as a strong ACE-I i...
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High-throughput and Rapid Screening of Novel ACE Inhibitory Peptides from Sericin Source and Inhibition Mechanism by Using in Silico and in Vitro Prescriptions Huaju Sun, Qing Chang, Long Liu, Kungang Chai, Guangyan Lin, Qingling Huo, Zhenxia Zhao, and Zhongxing Zhao J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b04043 • Publication Date (Web): 31 Oct 2017 Downloaded from http://pubs.acs.org on November 1, 2017

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Journal of Agricultural and Food Chemistry

High-throughput and Rapid Screening of Novel ACE Inhibitory Peptides from Sericin Source and Inhibition Mechanism by Using in Silico and in Vitro Prescriptions Huaju Sun, Qing Chang, Long Liu, Kungang Chai, Guangyan Lin, Qingling Huo, *

Zhenxia Zhao, Zhongxing Zhao

Guangxi Colleges and Universities Key Laboratory of New Technology and Application in Resource Chemical Engineering, School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China

*

Corresponding Authors Phone: +86-771-3233718; Fax: [email protected] (Zhongxing Zhao)

+86-771-3233718;

1

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E-mail:

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ABSTRACT: Several novel peptides with high ACE-I inhibitory activity were

2

successfully screened from sericin hydrolysate (SH) by coupling in silico and in vitro

3

approaches for the first time. Most screening processes for ACE-I inhibitory peptides

4

were achieved through high-throughput in silico simulation followed by in vitro

5

verification. QSAR models based predicted results indicated that the ACE-I inhibitory

6

activity of these SH peptides, and six chosen peptides exhibited moderate high ACE-I

7

inhibitory activities (LogIC50 values: 1.63 to 2.34). Moreover, two tripeptides among

8

the chosen six peptides were selected for ACE-I inhibition mechanism analysis which

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based on Lineweaver-Burk plots indicated to behave as competitive ACE-I inhibitor.

10

The C-terminal residues of short-chain peptides that contain more H-bond acceptor

11

groups could easily form hydrogen bonds with ACE-I and have higher ACE-I

12

inhibitory activity. Overall, sericin protein as a strong ACE-I inhibition source could

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be deemed a promising agent for antihypertension applications.

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KEYWORDS: sericin hydrolysis, in silico prediction, LC-MS, ACE-I inhibitory

15

peptides, molecular docking, inhibitory mechanism

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■ INTRODUCTION

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Hypertension is a major risk factor for developing cardiovascular diseases, and has

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become a serious threat to human health and quality of life recently. According to

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incomplete statistic from the World Health Organization (WHO), this chronic disease

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will increase up to 29 % of the world’s adult population till 2025.1 It has been

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reported that angiotensin-I-converting enzyme (ACE-I, EC 3.4.23.15) plays a key role

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in blood pressure control in renin-angiotensin-aldosterone system, which alternatively

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can monitor and attenuate hypertension.2 Recently, search for new ACE-I inhibitor

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peptides with high bioactivity from natural sources has attracted greater attention for

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hypertension treatment attributed to their minimal side-effects.3

27

Up till now, more than 700 types of ACE-I inhibitory peptides have been reported

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in BIOPEP database (http://www.uwm.edu.pl/biochemia/index.php/en/biopep). Most

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potent peptides were directly screened from enzymatic hydrolysates of traditionally

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edible proteins, such as milk proteins,4,5 marine organisms,6-11 meat,12,13 and plant

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proteins.14,15 However, purification process like gel chromatography, ion exchange

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chromatography and reverse phase high-performance liquid chromatography are

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hindered by complicated and time-consuming nature. Furthermore, the low

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purification efficiency and high cost of these approaches have seriously restricted

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their applications in industrial development. Moreover, screening for ACE-I

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inhibitory peptides is usually estimated from in vitro ACE-I inhibitory activity of

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hydrolysates, where the fraction with highest activity is chosen for further separation.

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However, among other problem associated with conventional extraction strategies,

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there exist a chance of missing some peptides with high activity.16 Thus, high

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throughput and rapid screening of novel peptides with high ACE-I inhibitory activity 3

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from natural sources has become a center of rich research area among scientific

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community dealing in separation and purification of peptides.

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With the development of sequencing technology and biomolecular simulation, an

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increasing number of researches are booming for predicting activity of peptides

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binding with ACE-I by using computer simulation. Compared to traditional methods,

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in silico analysis is considered as a high-throughput and rapid technique to

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preliminarily select bioactive peptides from known protein sequences. Based on some

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typical enzymes with known cleavage specificities, in silico approach has been used

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to predict bioactive peptides from natural proteins sources, such as prolyl

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endopeptidase inhibitory peptides,17 peptidase-IV inhibitory peptides and ACE-I

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inhibitory peptides from edible sources including meat by-products and milk

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proteins.18,19. However, not many reports are available about screening of novel and

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highly-active peptides from natural source with the assistance of in silico method.

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Sericin, as a major component of silkworm cocoon, has been reported to have great

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potential

for

many

biomedical

and

biological

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antibiotic-antibacterial activity,20 antioxidant behavior,21 anti-tyrosinase activity22 and

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anti-carcinogenic effects.23 To the best of our knowledge, its potential activity for

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ACE-I inhibitory property has not been reported so far. Thus, in this work, several

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novel peptides were successfully screened from sericin hydrolysate (SH) for ACE-I

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inhibition by coupling in silico and in vitro approaches. In silico was used to screen

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hydrolysate enzyme (PeptideCutter) for sericin protein, predict ACE-I inhibitory

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activity in terms of Quantitative structure activity relationship (QSAR), analyze

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toxicity and anti-digestion activity (ToxinPred and PeptideCutter) of the obtained

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peptides from virtual SH source using liquid chromatography – mass spectrometry

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(LC-MS) technique. The selected peptides with theoretically high ACE-I inhibitory 4

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applications,

such

as

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activity were synthesized and verified in vitro studies. Moreover, ACE-I inhibition

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pattern and ACE-I inhibition mechanism of the selected peptides were also

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systematically studied from classic Lineweaver-Burk model and molecular docking

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simulation.

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■ MATERIALS AND METHODS

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Materials. The silkworm cocoon waste was provided by Maoyuan silk cocoon Co.,

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Ltd. (Yizhou, China). Trypsin (EC 3.4.21.4, 250 U/mg) was purchased from Biodee

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Biotechnology Co. Ltd. (Beijing, China), while proteinase K (EC 3.4.21.64, 40 U/mg)

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and pepsin (pH>2) (EC 3.4.23.1, 2500 U/mg) were purchased from Solarbio Science

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& Technology Co., Ltd. (Beijing, China). The hippuryl-L-histidyl-L-leucine (HHL)

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and ACE-I (from rabbit lung) was supplied by Sigma-Aldrich Chemical Co. (St. Louis,

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MO, USA). The identified peptides were synthesized (98% purity) by GL. Biochem.

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Co., Ltd. Methanol for HPLC analysis was supplied by Thermo Fisher Scientific Co.,

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Ltd. (USA). All other chemicals and reagents used were of analytical grade without

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further treatment.

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Selection of proteases for hydrolysis using PeptideCutter analysis. Sericin was 24,25

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mainly composed of three proteins (Ser1, Ser2 and Ser3) as reported previously

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Among which, Ser1 contributed 90% of the total weight of cocoon proteins in the last

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larval instar. Therefore, we extracted the sequence of Ser1 from protein data

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UniProtKB (http://www.uniprot.org/) and protein database of the National Center for

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Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein).15 Then,

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Ser1 was consequently subjected to in silico hydrolysis using the program of

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PeptideCutter (http://web.expasy.org/peptide_cutter/) to identify the most appropriate

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proteases for sericin hydrolysis (SH). The top three proteases with the largest cleavage

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sites were chosen for sericin hydrolysis in vitro.

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Preparation of sericin hydrolysis in vitro. Sericin was extracted from silkworm

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cocoon waste autoclaving at 121 °C and 0.2 MPa (mention autoclave environment)

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for 2 h.26 Its ratio of solid to liquid was adjusted to 1:30 (W/V). After filtration, the

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extraction solution was lyophilized and stored under 5.0 °C. The extraction ratio of

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sericin was approximately 260-300 g/kg from silkworm cocoon. The hydrolyzed

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using the determined three proteases of pepsin (P), trypsin (T), proteinase K (K) and

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their bienzyme T+K system under hydrolysis conditions as shown in Table 1. In this

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work, both of single protease and bienzyme were used to hydrolyze sericin, which

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were named as one-stage and two-stage enzymatic hydrolysis, respectively. Finally,

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four SHs were obtained by using different protease systems. The ACE-I inhibitory

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activity of different SHs was tested, and the one with highest ACE-I inhibitory

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activity was further studied.

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Measurement of ACE-I inhibitory activity. The assay of ACE-I inhibitory activity

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in vitro was measured by Cushman’s method 27 with slight modifications. It is mainly

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based on the liberation of hippuric acid from hippuryl-L-histidyl-L-leucine

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(Hip-His-Leu) catalyzed by ACE-I. The IC50 value is defined as the concentration of

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peptides (µM) required to inhibit 50% of ACE-I activity.28 Each assay was performed

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in triplicate of independent determinations. The IC50 value of each SH was used to

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screen protease and resource of ACE-I inhibitory peptide (hydrolysate).

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Construction of QSAR model for rapid screening peptides with high ACE-I

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inhibitory activity. QSAR models of peptides with ACE-I inhibitory activity were

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constructed according to the method of Wei Qi 29 with slight modifications. Firstly, a 6

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total of 122 ACE-I inhibitory peptides (from di- to hexa- peptide) with known ACE-I

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inhibitory activity were retrieved from both the BIOPEP database and published

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literature.30 These peptides were classified into three data sets (train sets and test sets)

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according

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tetra/penta/hexa/peptides. Then, the most active peptide of each data set was chosen

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as the template, and the rest 119 peptides were aligned on template to form three

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common structures using Sybyl X-2.1.1 (Tripos Inc., St. Louis, MO, USA).

to

peptide

length,

including

dipeptides,

tripeptides

and

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Next, CoMFA and CoMSIA descriptor fields (steric, electrostatic, hydrophobic,

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donor and acceptor) were used to analyze three training sets, and the best QSAR

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model was identified by the partial least squares (PLS) methodology analysis with the

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leave-one-out (LOO) cross-validation procedure. The cross-validated coefficient (Q2)

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and standard deviation of error prediction (R2) were calculated according to Eq. (1)

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and Eq. (2):31 ∑( −   )  =1− ∑( −  ) 

 = 1 −

∑( −  ) ∑( −  )

Eq. (1)

Eq. (2)

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where Ymean is average activity of the entire data set, while Yobs, Ypred and YCVpred

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represent the observed, predicted and cross-validated activity values, respectively.

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LC-MS analysis for Sericin hydrolysate. SH with highest ACE-I inhibitory activity

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was used for peptide sequence identification by a nanoLC LTQ Orbitrap MS (Thermo

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Scientific, USA), following the stepwise method as: (1) SH was separated with a

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Hypersil C18 column (250×4.6 mm, particle size 5 µm); (2) the adsorbed components

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were eluted with a two solvent system: (A) 0.1% formic acid and 2.0% acetonitrile in

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water and (B) 0.1% formic acid and 2.0% water in acetonitrile with a gradient from 7

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2.0% solvent B to 15.0% solvent B over 5.0 min, to 35.0% over 20 min, to 98.0%

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over 5 min, maintained for 10 min at a constant flow rate of 250 nL/min; (3)

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ionization was performed by nanolockspray ionization source in positive ion mode

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(capillary voltage 3.80 kV and a source temperature of 280 °C; (4) an MS full-scan

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was performed for each sample with an acquisition range m/z of 50-800 Da.

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ACE-I inhibitory activity prediction and validation. The constructed QSAR model

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was used to predict ACE-I inhibitory activity of identified peptides from SH

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hydrolysate by the combination of T and K. Nine peptides with various ACE-I

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inhibitory activities from QSAR prediction were chosen, and their toxicity and

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digestive

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ToxinPred(http://www.imtech.res.in/raghava/toxinpred/)32

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Finally, eight peptides with nontoxicity and anti-digestion activity were synthesized

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by GL. Biochem. Co., Ltd. (Shanghai, China) using conventional solid-phase

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chemistry and validated their activities in vitro.

stability

were

subsequently

simulated and

by

PeptideCutter.18

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Kinetics of ACE-I Inhibition. Among the six selected peptides with the highest

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ACE-I inhibitory activity, the highest and lowest ACE-I inhibitory active ones (named

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A and B) were inspected for the inhibition kinetics by Lineweaver-Burk plot.33

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Briefly, ACE-I inhibition tests were conducted in the presence of different

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concentrations of HHL and peptides solution. The varying concentration of the

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enzyme substrate HHL (0.62, 1.10, 1.64 and 1.97 mM) was reacted with ACE-I in the

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absence and presence of two different concentrations of inhibitory peptide A (108.89

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µM and 163.34 µM) and peptide B (93.77 µM and 140.65 µM).34,35

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Molecular docking of the selected peptides with ACE-I. Molecular docking was

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performed to investigate conformation between ACE-I active sites and inhibitors 8

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using the flexible docking tool of Sybyl X-2.1.1 program package, and the structure

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was energy minimized using the Powell conjugate gradient optimization algorithm

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with the Tripos force field.36 The three-dimensional crystal structure of

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ACE-I-lisinopril complex was obtained from the Protein Data Bank (PDB: 1O86,

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http://www.rcsb.org/pdb/home/home.do) by only removing the inhibitor lisinopril and

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water molecule. Then the protein structure was pre-analyzed and prepared for the

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docking runs using biopolymer structure preparation tool with default settings and the

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protocol was created automatically. The Surflex-Dock program was used for docking

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studies. The receptor-ligand interactions of the ligand were evaluated by the software

168

in terms of Total Score and expressed as LogKd, where Kd is binding constant, where

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high value of Total Score represents good protein-ligand binding and vice versa. The

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conformations of ranked No. 1 (the highest Total Score) was selected and the

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interaction model between the ACE-I residues and peptides was shown by PyMOL

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v1.8.6 (http://www.pymol.org/).

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Statistical analysis. All assays of ACE-I inhibitory activity were conducted in

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triplicates. Data were presented as mean ± standard deviation. The statistical analysis

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was performed using SPSS 10.0 software (SPSS Inc., Chicago, IL, USA). Significant

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difference in means between the samples was determined at a 5% confidence level (p

177

< 0.05).

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■ RESULTS AND DISCUSSION

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Amino acid composition of Ser1 and screening enzymes for sericin hydrolysis.

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Figure S1(A) shows the sequence of Ser1, obtained from protein data UniProtKB and 9

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protein database of NCBI. Its amino acid (AA) composition was counted using

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ProtParam (http://web.expasy.org/protparam/), and the results are shown in Figure 1

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(A). It can be seen that sericin possesses the most abundant AA in Ser1 protein,

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holding a minimum 32 % of Ser1. Hydrophobic AAs (red columns) account for 30.0%

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of Ser1 except serine, which could count for high activity of ACE-I inhibitors due to

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the presence of more hydrophobic AAs in the protein.37

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Ser1 protein was consequently subjected to in silico hydrolysis using PeptideCutter

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to determine the most suitable proteases for in vitro sericin hydrolysis. In this work,

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about 38 available enzymes and chemicals (Table S1) supplied from this program,

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were all chosen for theoretic hydrolysis simulation. The top five proteases with largest

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cleavage sites are shown in Table S2. The order of cleavage site number was:

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proteinase K > pepsin (pH>2) > trypsin > clostripain > pepsin (pH 1.3). Next,

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proteases of proteinase K (K), trypsin (T), and pepsin (pH>2) (P) were chosen for

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sericin hydrolysis in experiment due to its possessing most cleavage sites which

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possibly release most ACE-I inhibitory peptides from protein.38

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After being hydrolyzed with different proteases in vitro, four hydrolysates were

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prepared and their ACE-I inhibitory activities were tested. Figure 1B shows their

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LogIC50 values corresponding to different hydrolysis conditions with single protease

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and biprotease. In one-stage hydrolysis with single protease, hydrolysates show the

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ACE-I inhibitory activity in the following order of preference: K > T > P. In the next

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experiment, proteinase K and trypsin (K+T) were used to hydrolyze sericin in

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two-stage hydrolysis. As expected, its hydrolysate shows the highest ACE-I inhibition

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activity. About, 137 peptides in its hydrolysate were obtained and their sequence

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distribution is shown in Figure S1(B). Consistent with that observed by simulation 10

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(Table S2), more cleavage sites may have contributed towards the higher ACE-I

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inhibition activity.

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QSAR model for prediction of peptide ACE-I inhibitory activity. Amino acids at

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C-terminal end usually contribute significantly to ACE-I inhibitory activity.39,40 The

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C-terminal residues of 137 peptides from Ser1 hydrolysates (K+T) using in silico

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were analyzed and shown in Table S3, where most were composed of hydrophobic

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AA (50-60 % of the total), basic AA and acidic AA. Based on these results, some

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peptides with known ACE-I inhibitory activity

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useful peptides as the training and test sets for QSAR model construction were chosen.

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These 122 peptides possessing the same C-terminal residues, which composed of 38

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dipeptides, 39 tripeptides and 45 peptides length between 4 and 6, are listed in Table

217

S4. Peptides of 13th (LogIC50=0.21), 57th (LogIC50=0.36) and 96th (LogIC50=0.18)

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have the highest activity of ACE-I inhibition for each training set. Thus, these three

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peptides were selected as the template, and the rest of peptides in each training set

220

were aligned onto templates (peptide 13th, 57th and 96th) to form three

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superimpositions. Based on three common structures, 122 peptides were analyzed by

222

CoMFA and CoMSIA, respectively.

30

were collected, among which 122

223

Subsequently, the stepwise development of CoMFA and CoMSIA models using

224

five descriptor fields i.e. steric, electrostatic, hydrophobic, acceptor and donor fields,

225

41,42

226

tetra/penta/hexapeptides, respectively, while the statistical parameters for the best

227

QSAR CoMFA/CoMSIA models of each data set are shown in Table 2. As seen in

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QSAR

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tetra/penta/hexa-peptides were corresponding to CoMSIA, CoMFA and CoMSIA

is

presented

analysis,

in

the

Table

best

S5-S7

for

predictions

dipeptides,

for

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tripeptides,

dipeptides,

and

tripeptides,

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230

models, respectively. The optimum model for each data set showed all high statistical

231

significances (Q2 >0.73 and R2 >0.99), indicating a proof of high predictive ability of

232

the constructed model.43

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Moreover, the contributions of five descriptor fields for each data set are also

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shown in Table 2. Clearly, the contribution of each field to peptide ACE-I inhibitory

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activity was different for each data set. Dipeptide set was contributed by the steric,

236

electrostatic and acceptor fields prominently; tripeptide set was completely controlled

237

by steric field; while tetra/penta/hexapeptide set was mainly influenced by

238

electrostatic, hydrophobic and donor fields. The scatter plots of observed against

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predicted LogIC50 values for the best QSAR model were displayed in Figure

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2(A-C).44,45 This analysis showed that the obtained data points were uniformly

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distributed along the regression line with small prediction errors ( 2), trypsin and chymotrypsin,

303

among which, only SSY could be cleaved during simulation of GI digestion. Thus, the

304

remaining eight stable peptides (SSR, SSK, GSR, SGR, NPR, SSSNSV, SSDA and 14

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DE) were further synthesized and validated for their ACE-I inhibitory activities in

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vitro (Table 4). It can be seen that all the synthesized peptides exhibited similar tested

307

LogIC50 value to predicted one with small prediction errors (