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Journal of Zhejiang University. Science. B logoLink to Journal of Zhejiang University. Science. B
. 2021 Jul 15;22(7):575–589. doi: 10.1631/jzus.B2000520

Inhibitory mechanism of angiotensin-converting enzyme inhibitory peptides from black tea

Yating LU 1, Yu WANG 1, Danyi HUANG 1, Zhuang BIAN 1, Peng LU 2, Dongmei FAN 1, Xiaochang WANG 1,✉
PMCID: PMC8284085  PMID: 34269010

Abstract

The aim of this work is to discover the inhibitory mechanism of tea peptides and to analyse the affinities between the peptides and the angiotensin-converting enzyme (ACE) as well as the stability of the complexes using in vitro and in silico methods. Four peptide sequences identified from tea, namely peptides I, II, III, and IV, were used to examine ACE inhibition and kinetics. The half maximal inhibitory concentration (IC50) values of the four peptides were (210.03±18.29), (178.91±5.18), (196.31±2.87), and (121.11±3.38) μmol/L, respectively. The results of Lineweaver-Burk plots showed that peptides I, II, and IV inhibited ACE activity in an uncompetitive manner, which requires the presence of substrate. Peptide III inhibited ACE in a non-competitive manner, for which the presence of substrate is not necessary. The docking simulations showed that the four peptides did not bind to the active sites of ACE, indicating that the four peptides are allosteric inhibitors. The binding free energies calculated from molecular dynamic (MD) simulation were -72.47, -42.20, -52.10, and -67.14 kcal/mol (1 kcal=4.186 kJ), respectively. The lower IC50 value of peptide IV may be attributed to its stability when docking with ACE and changes in the flexibility and unfolding of ACE. These four bioactive peptides with ACE inhibitory ability can be incorporated into novel functional ingredients of black tea.

Keywords: Black tea, Angiotensin-1-converting enzyme (ACE) inhibitory peptide, Kinetic study, Molecular docking, Molecular dynamic (MD) simulation

1 Introduction

Hypertension is the leading preventable cause of premature death worldwide because of its high prevalence and resulting complications (Mills et al., 2016). Long-term high blood pressure is normally considered a major risk factor for cardiovascular diseases (CVDs) such as atherosclerosis, coronary heart disease, stroke, and heart failure (Daskaya-Dikmen et al., 2017). It has been indicated that treatments that lower blood pressure can significantly reduce the risk of CVDs (Ettehad et al., 2016). Currently, angiotensin-converting enzyme (ACE; EC 3.4.15.1) is thought to perform a crucial function in controlling blood pressure (Hanif et al., 2010). ACE is a key component of the renin angiotensin system (RAS), an essential hormone system responsible for the homeostasis of blood pressure in mammals. It removes the C-terminal dipeptide from its substrate precursor peptide angiotensin I (AngI) to produce AngII, which is a potent vasoconstrictor and blocks the vasodilating properties of bradykinin, ultimately causing an increase in blood pressure (Harrison and Acharya, 2014). The first ACE inhibitors were found from the snake venom of Bothrops jararaca (Ferreira, 1965). Thereafter many synthetic ACE inhibitors, including captopril, enalapril, and lisinopril, were developed as effective antihypertensive drugs. However, they may cause certain adverse effects, including coughing, allergic reactions, taste disturbances, and skin rashes (Bougatef et al., 2008). People suffering from prehypertension (resting blood pressures between 120 mmHg/80 mmHg and 139 mmHg/89 mmHg (1 mmHg=0.133 kPa)) usually do not accept drug treatment because of the side effects and costs. However, lifestyle change including doing more exercise and taking healthy nutrients is urgently needed (Collier and Landram, 2012; Tao C et al., 2017). Thus, there is a growing interest in discovering ACE inhibitors in natural products as alternatives or as co-adjuvant to synthetic drugs.

The identification of bioactive ACE inhibitory peptides derived from food proteins has become popular in recent years because of the excellent efficacy and safety of these peptides. They are inactive within the parent protein but exert positive physiological effects on systems of the body when released (Lafarga and Hayes, 2017). A large number of bioactive peptides have been isolated from bacteria, fungi, plants, animals, and even human bodies (Gu et al., 2011). The online database BIOPEP-UWM (http://www.uwm.edu.pl/biochemia) contains fundamental information about sequence databases and tools for the evaluation of proteins as the precursors of bioactive peptides. Over nine hundred bioactive peptides have been reported as ACE inhibitors in the BIOPEP database thus far. Most are from food sources, such as milk (Li et al., 2015), chicken muscle, tuna, soy, garlic, wheat, and olive seeds (Minkiewicz et al., 2008). Several studies have also reported in vivo ACE inhibitory activity of peptides (Escudero et al., 2013; Majumder et al., 2015). Therefore, ACE inhibitory peptides derived from food have received greater recognition and have been studied closely as potent natural and healthier alternatives to ACE inhibitor drugs.

More recently, in order to circumvent some challenges of the classical approach and interpret the mechanisms of action of novel ACE inhibitory peptides, in silico approaches have been widely used. For example, molecular docking has been applied to study the active mechanisms by which certain food-derived peptides inhibit ACE activity (Sornwatana et al., 2015). Molecular dynamic (MD) simulation has been used to further explore the interactions between inhibitors and receptors from a metabolic perspective (Guan et al., 2016).

Tea is a traditional health beverage in China, and its antioxidant (Satoh et al., 2005), antidiabetic (Wang et al., 2010; Zhang et al., 2010), and anti-hypertensive (Li et al., 2019) functions have been confirmed. In our previous studies, black tea was examined as a source of bioactive peptides for its special manufactory process such as fermentation, and four peptides (QTDEYGNPPR, AGFAGDDAPR, IDESLR, and IQDKEGIPPDQQR) with dipeptidyl-peptidase IV (DPP-IV) inhibitory activity were purified and identified using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and liquid chromatography tandem mass spectrometer (LC-MS/MS) technologies (Lu et al., 2019). Certain peptides have been reported that are capable of exerting multiple types of physiological activity (Patil et al., 2015). For example, GYGGVSLPEW and LKPTPEGDLE derived from whey protein were reported as effective at inhibiting both ACE (half maximal inhibitory concentration (IC50)=2 μmol/L) and DPP-IV (IC50=42 μmol/L) (Lacroix et al., 2016). Diabetes and hypertension are believed to share a common pathway and a person with diabetes is more likely to suffer high blood pressure (Cheung and Li, 2012). Thus, in this study, we plan to investigate the ACE inhibitory activity of black tea peptides in vitro and to explore their inhibition patterns and mechanisms using the classic Lineweaver-Burk model as well as molecular docking/dynamic simulation. This study may provide a theoretical basis and experimental evidence for peptides in black tea as novel bioactive components and provide further evidence for black tea as a type of functional food.

2 Materials and methods

2.1. Materials

N-‍[3-‍(2-furyl)acryloyl]‍-L-phenylalanyl-glycyl-glycine (FAPGG; Lot: F7131), ACE (EC 3.4.15.1) from rabbit lung (Lot: A6778), and captopril (Lot: C8856) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Black tea derived peptide sequences (QTDEYGNPPR, AGFAGDDAPR, IDESLR, and IQDKEGIPPDQQR) were synthesized by the Chinese Peptide Company (Hangzhou, China) with a purity of 96%‒97%.

2.2. Assay for ACE inhibitory activity

ACE inhibitory activity was measured according to the method described by Shalaby et al. (2006) with minor modifications. Briefly, 10 μL of the ACE solution (final activity of 12.5 U/L) was mixed with 40 μL of the peptide solution (final concentrations were 100 to 700 μg/mL, dissolved in ultrapure water) in the wells of a 96-well microtiter plate at room temperature. Then, 150 μL of 0.88 mmol/L FAPGG (dissolved in 50 mmol/L Tris-HCl buffer containing 0.3 mol/L NaCl, pH 7.5, preheated at 37 ℃, for 15 min) was added to each well in less than 1 min. Next, the microtiter plate was immediately transferred to the microplate reader (Synergy H1, BioTek Instruments Inc., Vermont, USA). The rate of the decrease in absorbance at 345 nm was recorded for 30 min in 1-min intervals at 37 ℃. For control groups, 40 μL of buffer (50 mmol/L Tris-HCl buffer with 0.3 mol/L NaCl, pH 7.5) was used instead of the peptide solution. ACE activity was expressed as the slope of the decrease in absorbance (ρA), and the inhibitory activity was calculated according to the formula:

ACE inhibition=(1-ρAinhibitor/ρAcontrol)×100%,

where ρA inhibitor and ρA control are the slopes obtained in the presence and absence of peptides, respectively. The IC50 values of tested samples were determined by the plot of ACE inhibition (%) against lg(sample concentration).

To determine the modes of ACE inhibition, the experiment was carried out with a set of concentrations of FAPGG substrate (0.88, 0.44, 0.22, and 0.11 mmol/L). V 0 is defined as the amount of FAPGG hydrolysed by ACE in 1 min at 37 ℃. The values of V 0 were measured in the absence and presence of peptide I (0.255 and 0.340 μmol/L), peptide II (0.205 and 0.307 μmol/L), peptide III (0.273 and 0.410 μmol/L), and peptide IV (0.273 and 0.410 μmol/L). The inhibition patterns of peptides were determined by Lineweaver-Burk plots. Non-competitive inhibitor, which does not necessarily need the presence of substrate, will change both the slope and the y-intercept. Uncompetitive inhibitor, which requires the presence of substrate, changes the y-intercept but not the slope. Competitive inhibitor changes the slope but not the y-intercept.

2.3. Molecular docking simulation

The structures of the peptides were obtained using the PEP-FOLD peptide structure prediction server (Shen et al., 2014). The crystal structures of ACE and captopril were obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB; ACE was obtained from PDB structure 4APH and captopril was obtained from PDB structure 1UZF). The structure of FAPGG was obtained from the PubChem database. The structure removing glycyl-glycine (GG) from FAPGG was used as the structure of N-[3-(2-furyl)acryloyl]‍-L-phenylalanyl (FAP). The ACE protein was prepared by removing water molecules and co-crystal ligands. Molecular docking was performed using AutoDock Vina Version 1.1.2 (Trott and Olson, 2010), A grid of 40×40×40 points in the x-, y-, and z-axis directions was built with a grid spacing of 1 Å. Docking FAPGG to ACE was performed before the docking of peptides I, II, and IV because they were uncompetitive inhibitors. The mode with the lowest affinity value was chosen as the final docking result.

2.4. Molecular dynamic simulation

All the complex systems were subjected to MD simulation using the Amber 17 project (Case et al., 2005). Each complex was solvated in an octahedral box full of TIP3P water molecules. Sodium ions were placed to keep the whole system neutral and at minimum energy. The leaprc.protein.ff14SB force field was used to perform the 100 ns MD simulation. The coordinate bonds between Zn (II) and its surroundings were treated by the Metal Center Parameter Builder (MCPB) method (Yu et al., 2019). The MD simulations at 300 K were performed for 100 ns, and the MD time step was taken as 0.02 s; thus 5000 conformations were obtained in each MD simulation.

2.5. Binding free energy and ligand‒residue interaction decomposition calculation

After simulation, the binding free energies were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) approach in Amber 17. This is suitable for various molecular systems. The concepts and the practical procedures of this approach have been described in detail elsewhere (Homeyer and Gohlke, 2012). A total of 100 snapshots were extracted from the 5000 MD trajectories (100 ns) equably for calculation, and the interval was 50 trajectories. The interactions between each ligand (peptide) and every residue in the ACE protein were analysed using the MM-PBSA decomposition process applied in the MM-PBSA module in Amber 17. The snapshots extracted from this procedure were the same as those in the binding free energy calculations.

2.6. Statistical analysis

The assays of ACE inhibitory activity were carried out with three replications and data were presented as mean±standard deviation (SD). Significant differences of ACE inhibitory activity were analysed using Ryan-Einot-Gabriel-Welsch multiple range (REGWQ) test (P<0.05) in Statistical Analysis System (SAS) University Edition (Institute Inc., Cary, NC, USA).

3 Results

3.1. ACE inhibitory activity of tea peptides

Four synthetic peptides identified in black tea were tested for ACE inhibitory activity at concentrations ranging from 100 to 700 μg/mL. The unit of concentration was converted to μmol/L according to the peptide molecular weights. The IC50 values for four peptides are presented in Table 1. Tridecapeptide IQDKEGIPPDQQR (peptide IV) exhibited the strongest inhibitory capacity with the lowest IC50 value of (121.11±3.38) μmol/L, compared to AGFAGDDAPR (peptide II, (178.91±5.18) μmol/L), the hexapeptide IDESLR (peptide III, (196.31±2.87) μmol/L), and decapeptide QTDEYGNPPR (peptide I, (210.03±18.29) μmol/L). The IC50 value of captopril (positive control) was (21.78±1.10) nmol/L, which was consistent with the IC50 range obtained by others (7–22 nmol/L) (Shalaby et al., 2006).

Table 1.

Molecular weight, half maximal inhibitory concentration (IC50) values, and binding free energy of four peptides

Sample Peptide sequence Molecular mass (Da) IC50 Binding free energy (kcal/mol)
Peptide I QTDEYGNPPR 1176 (210.03±18.29)a μmol/L -72.47
Peptide II AGFAGDDAPR 976 (178.91±5.18)b μmol/L -42.20
Peptide III IDESLR 731 (196.31±2.87)ab μmol/L -52.10
Peptide IV IQDKEGIPPDQQR 1523 (121.11±3.38)c μmol/L -67.14
Captopril 217 (21.78±1.10) nmol/L -24.20
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The IC50 values are expressed as mean±standard deviation (SD), n=3. Means with the same letters (a, b, c) are not significantly different (P>0.05). 1 kcal=4.186 kJ.

3.2. Kinetics of ACE inhibition activity

The ACE inhibition patterns of the four peptides were evaluated by Lineweaver-Burk plots. The regression curves of different peptide concentrations were parallel for peptides I, II, and IV, exhibiting uncompetitive inhibition (Figs. 1a, 1b, and 1d). The regression curves of different concentrations of peptide III intersected at the 1/[S] axis, indicating that the inhibition was non-competitive (Fig. 1c).

Fig. 1. Angiotensin-converting enzyme (ACE) inhibition patterns of peptides I, II, III, and IV at different concentrations were determined using Lineweaver-Burk plots. V 0 is defined as the amount of FAPGG hydrolysed by ACE in 1 min at 37°, and C S means the concentration of FAPGG substrate. (a) Peptide I, uncompetitive inhibition pattern; (b) Peptide II, uncompetitive inhibition pattern; (c) Peptide III, non-competitive inhibition pattern; (d) Peptide IV, uncompetitive inhibition pattern.

Fig. 1

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3.3. Molecular docking of tea peptides and ACE

The different IC50 values and inhibition patterns among the four peptides indicated that these peptides inhibit ACE through different mechanisms. Therefore, molecular docking was performed to further investigate the interactions and to predict the preferred orientations of these four peptides when they bind to ACE. The best docking modes with the lowest affinity values are shown in Fig. 2. Peptides I, II, III, and IV were all located in a long cleft in ACE away from the active pockets. The interactions of ACE with four peptide ligands were detected and plotted using the LigPlot+ program (Laskowski and Swindells, 2011). The results presented in Fig. 3 show that interactions including hydrogen bonds, hydrophobic forces, and van der Waals contacts can be observed. Fifteen hydrogen bonds were formed between amino acids of peptide I and residues of ACE (Fig. 3a). Six hydrogen bonds were found between amino acidsof peptide II and residues of ACE in Fig. 3b. Only two hydrogen bonds were formed between peptide III and ACE (Fig. 3c), while peptide IV was surrounded by an extensive network of interactive residues from ACE (Fig. 3d). The carbonyl oxygen atoms and amidogen hydrogen atoms of Gln2IV contributed four hydrogen bonds, Ile1IV, Pro9IV, and Asp10IV contributed two hydrogen bonds each. Lys4IV and Arg13IV were stabilized with residues Thr92ACE and Lys118ACE by one hydrogen bond interaction. On the other sides, captopril was docked in the cavity near the active pockets and formed hydrogen bonds with Tyr520ACE and Gln281ACE (Fig. 2).

Fig. 2. Binding sites of peptides I, II, III, IV, captopril, N-‍[3-‍(2-furyl)acryloyl]‍-L-phenylalanyl-glycyl-glycine (FAPGG), and N-[3-(2-furyl)acryloyl]-L-phenylalanyl (FAP) within the structure of angiotensin-converting enzyme (ACE). Peptides I, II, III, and IV are shown as ribbons colored in blue, red, magenta, and orange, respectively. FAPGG, FAP, and captopril are shown as sticks colored in red, yellow, and green, respectively. The ACE enzyme is shown as a cartoon in grey and cyan according to the root mean square fluctuation (RMSF) value. Zn (II) in ACE is shown as a red dot. The active pockets of ACE are colored in wheat.

Fig. 2

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Fig. 3. Schematic representation of interactions across the interfaces of peptide‒angiotensin-converting enzyme (ACE) complexes generated using LigPlot+. (a) Peptide I‒ACE complex; (b) Peptide II‒ACE complex; (c) Peptide III‒ACE complex; (d) Peptide IV‒ACE complex.

Fig. 3

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3.4. Molecular dynamic simulation of tea peptides and ACE

The conformations selected from molecular docking were performed with dynamic simulation to further validate their interaction mechanisms. Total binding free energies and contributions of residues surrounding peptides were calculated by the MM-PBSA approach. The binding free energy of peptide I was the lowest (‍-‍72.47 kcal/mol, 1 kcal=4.186 kJ), followed by peptide IV (-67.14 kcal/mol) and peptide III (-52.10 kcal/mol), and the value of peptide II was the highest (-42.20 kcal/mol) (Table 1).

The van der Waals (ΔE vdw), electrostatic (ΔE ele), polar solvation (ΔE sol), and total (ΔE total) contributions of the residues to the binding free energy of the ACE‒peptide complex were also calculated by the MM-PBSA method. Electrostatic interactions play an important part in the formation of hydrogen bonds; in the presence of strong electrostatic interactions, several hydrogen bonds can easily form (Guan et al., 2016). Arg124ACE contributes the most electrostatic energy to the peptide I–ACE complex (Fig. 4a). Trp59 ACE and Arg124ACE both contributed remarkable total energy to the combinations of peptide II‍–‍ACE (Fig. 4b). Arg124ACE, Lys368ACE, and Arg522ACE all contributed both electrostatic and total energies (Fig. 4c). Trp59ACE, Tyr62ACE, Glu143ACE, Trp357ACE, and Ser516ACE show clear total energy values in Fig. 4d. In addition, Lys118ACE, Asp121ACE, and Arg522ACE make significant electrostatic contributions.

Fig. 4. Decomposition of binding energy per residue based on peptide I‒angiotensin-converting enzyme (ACE) (a), peptide II‒ACE (b), peptide III‒ACE (c), and peptide IV‒ACE (d). ΔE vdw: van der Waals contribution to the binding free energy; ΔE ele: electrostatic contribution to the binding free energy; ΔE sol: polar solvation contribution to the binding free energy; ΔE total: total contribution to the binding free energy; ΔG bind: binding energy.

Fig. 4

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The initial binding orientations of peptides may change at different phases during simulation. Thus, the numbers of hydrogen bonds between ACE and inhibitory peptides were further confirmed by the results of MD simulations (Fig. 5). Peptide I can form more hydrogen bonds with ACE than can the other three peptides. Over ten hydrogen bonds can be constantly observed during the entire MD process, and the maximum number of hydrogen bonds formed was 25. The number of hydrogen bonds with ACE of peptide IV was fewer than that of peptide I but more than those of peptides II and III. The probabilities and details of occurrence of hydrogen bonds are shown in Table 2. Peptide I can form hydrogen bonds with 19 residues of ACE. Peptide II can form hydrogen bonds with 15 residues of ACE. However, the most presence of hydrogen bonds during MD simulation between peptide II and ACE was only 45.56%. Ser355ACE, Arg522ACE, Asn66ACE, Trp357ACE, Arg124ACE, Glu384ACE, Lys368ACE, Glu403ACE, and Hie513ACE might form hydrogen bonds with peptide III. Peptide IV has a probability of occurrence of hydrogen bonding with 14 residues of ACE, including Asn66ACE, Asn85ACE, Lys118ACE, Glu123ACE, Arg124ACE, Tyr135ACE, Leu139ACE, Glu143ACE, Ser355ACE, Ala356ACE, Glu403ACE, Gly404ACE, Ser516ACE, and Arg522ACE.

Fig. 5. Numbers of hydrogen bonds between angiotensin-converting enzyme (ACE) and peptides during molecular dynamic (MD) simulation: (a) peptide I; (b) peptide II; (c) peptide III; and (d) peptide IV. The line of 10 hydrogen bonds was set as the reference line.

Fig. 5

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Table 2.

Hydrogen bond occupancies for ACE‒peptide complexes

Acceptor Donor Presence (%) Acceptor Donor Presence (%)
Peptide I  Glu4IOE2 Ala125N 7.68
 Arg10IO Arg522NH2 86.32  Arg10IOXT Tyr523OH 7.54
 Glu4IOE1 Arg124NH2 69.46  Asp3IO Arg124NH2 5.42
 Arg10IO Arg522NH1 68.30 Peptide II
 Asn70OD1 Arg10INH2 64.40  Asp6IIOD2 Arg124NH2 45.56
 Ser517O Tyr5IOH 59.00  Asp7IIO Asn85ND2 40.08
 Asp3IOD2 Tyr360OH 56.28  Glu403OE2 Phe3IIN 38.82
 Ser516OG Arg10INE 54.20  Glu403OE1 Gly2IIN 38.06
 Glu143OE1 Arg10INH2 46.94  Asp6IIO Arg124NE 33.52
 Tyr5IO TyR62OH 46.22  Leu122O Asp6IIN 29.72
 Arg10IO Tyr523OH 44.92  Asp6IIHB3 Ala125N 27.26
 Glu4IOE1 Arg124NE 44.48  Ser516O Arg10IINH1 23.72
 Glu143OE2 Arg10INH2 44.38  Asp7IIOD1 Tyr62OH 21.66
 Asn70OD1 Arg10INH1 42.02  Arg10IIO Asn66ND2 18.64
 Glu4IOE2 Thr92OG1 41.38  Arg10IIOXT Ser355OG 18.62
 Asp3IOD1 Tyr360OH 41.04  Asp6IIOD2 Asn85ND2 18.06
 Arg10IOXT Hie353NE2 40.16  Asp7IIO Arg124NH1 17.56
 Ser517OG Tyr5IOH 29.20  Arg10IIO Trp357NE1 17.06
 Ser516HG Arg10INE 27.28  Arg10IIOXT Asn66ND2 16.64
 Asn7IOD1 Ser517OG 25.54  Asp7IIO Arg124NH2 16.42
 Glu4IOE1 Thr92OG1 25.06  Glu403OE2 Gly2IIN 14.76
 Glu4IOE2 Arg124NE 24.94  Asp6IIOD1 Arg124NH2 14.24
 Leu139O Asn7IND2 24.00  Arg10IIOXT Trp357NE1 13.84
 Arg402O Gln1IN 22.34  Asp358OD1 Ala1IIN 13.14
 Arg402O Gln1IN 22.00  Gly2IIO Tyr360OH 13.12
 Glu143OE1 Asn7IND2 21.64  Arg10IIO Ser355OG 12.90
 Tyr5IOH Tyr200OH 21.02  Asp6IIO Arg124N 12.88
 Arg402O Gln1IN 20.70  Ala1IIO Tyr360OH 12.78
 Glu143OE2 Asn7IND2 19.84  Asp358OD1 Ala1IIN 12.54
 Arg10IOXT Hie513NE2 18.60  Asp358OD1 Ala1IIN 12.42
 Ser516OG Asn7IND2 18.30  Phe3IIO Lys118NZ 11.22
 Asp3IO Trp59NE1 15.92  Phe3IIO Lys118NZ 10.04
 Glu403OE1 Gln1IN 15.02  Phe3IIO Lys118NZ 10.04
 Glu403OE1 Gln1IN 14.76  Arg402O Ala1IIN 7.34
 Glu403OE1 Gln1IN 13.34  Arg10IIOXT Lys368NZ 6.90
 Asp3IOD1 Lys118NZ 13.02  Arg402O Ala1IIN 6.80
 Asp3IOD1 Lys118NZ 10.20  Arg10IIO Asn70ND2 6.76
 Asp3IOD1 Lys118NZ 9.82  Arg10IIOXT Lys368NZ 6.48
 Tyr62OH Thr2IOG1 9.72  Arg402O Ala1IIN 6.44
 Arg10IIOXT Lys368NZ 5.82  Gln2IVO Arg124NH1 38.26
 Ser516O Arg10IINH2 5.62  Glu123OE1 Arg13IVNH2 34.76
 Arg10IIO Lys368NZ 5.06  Glu143OE1 Gln2IVNE2 31.94
Peptide III  Glu123OE2 Arg13IVNH2 31.10
 Arg6IIIO Ser355OG 98.94  Glu403OE2 Gln11IVNE2 29.98
 Glu3IIIOE2 Arg522NH1 65.20  Glu143OE2 Gln2IVNE2 29.04
 Ser4IIIO Asn66ND2 62.36  Asp3IVO Arg124NH1 25.42
 Arg6IIIO Trp357NE1 59.90  Asp10IVOD1 Arg522NH2 17.86
 Glu3IIIOE1 Arg522NH2 59.24  Glu143OE1 Ile1IVN 17.16
 Asp2IIIOD2 Arg124NH2 58.94  Asp10IVOD1 Arg522NH1 17.14
 Glu384OE2 Arg6IIINH2 39.58  Glu143OE2 Ile1IVN 15.98
 Arg6IIIO Lys368NZ 28.68  Glu143OE1 Ile1IVN 15.96
 Glu384OE1 Arg6IIINH2 49.50  Glu143OE1 Ile1IVN 15.52
 Asp2IIIOD1 Arg124NH1 45.90  Glu403OE1 Gln11IVNE2 15.20
 Glu384OE2 Arg6IIINH1 42.26  Glu143OE2 Ile1IVN 15.08
 Glu384OE1 Arg6IIINH1 42.20  Gln11IVO Arg522NH2 15.04
 Glu403OE2 Ile1IIIN 15.04  Asn66OD1 Gln2IVNE2 14.64
 Glu403OE1 Ile1IIIN 14.20  Asp10IVOD2 Ser355OG 13.94
 Asp2IIIOD2 Arg124NH1 9.02  Gln11IVOE1 Gly404N 11.64
 Arg6IIIOXT Lys368NZ 7.02  Asp3IVOD2 Tyr135OH 11.34
 Arg6IIIO Lys368NZ 28.46  Glu143OE2 Ile1IVN 10.88
 Arg6IIIO Lys368NZ 22.70  Arg13IVO Lys118NZ 9.88
 Glu3IIIOE1 Arg522NH1 19.96  Asp10IVO Arg522NH2 9.68
 Glu403OE1 Ile1IIIN 15.08  Gln2IVOE1 Tyr62OH 9.28
 Arg6IIIOXT Lys368NZ 6.94  Arg13IVO Lys118NZ 7.88
 Leu5IIIO Hie513NE2 6.40  Glu123OE1 Lys4IVNZ 7.84
 Arg6IIIOXT Lys368NZ 6.06  Gly6IVO Tyr360OH 7.68
 Arg6IIIOXT Hie513NE2 5.78  Arg13IVO Lys118NZ 7.60
 Glu403OE1 Ile1IIIN 13.70  Glu123OE2 Arg13IVNH1 7.60
 Glu403OE2 Ile1IIIN 12.44  Ser516O Ile1IVN 7.22
 Glu403OE2 Ile1IIIN 11.04  Ser516O Ile1IVN 7.18
 Glu3IIIOE2 Arg522NH2 9.78  Arg13IVOXT Lys118NZ 6.78
 Asp2IIIOD1 Arg124NH2 5.66  Ala356O Asp10IVN 6.72
Peptide IV  Ser516O Ile1IVN 6.68
 Asp10IVO Arg522NH1 71.60  Ser516OG Ile1IVN 6.08
 Gln2IVO Arg124NH2 67.92  Arg13IVOXT Lys118NZ 5.90
 Gly404O Gln11IVNE2 60.88  Arg13IVOXT Lys118NZ 5.80
 Gln11IVO Arg522NH2 58.74  Leu139O Gln2IVNE2 5.64
 Gln2IVOE1 Asn85ND2 47.36  Asn66OD1 Gln2IVN 5.32
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The stability of the ACE protein/peptide complexes during their binding processes was predicted using root mean square deviation (RMSD) analysis. Five thousand trajectories obtained from the 100-ns dynamic simulation were used in this analysis. The RMSD of ACE increased after inhibitors (peptides and captopril) bound to it (Fig. 6a). However, the RMSD of ACE combined with captopril rose and fluctuated very slightly. The RMSD of ACE combined with peptide IV increased to a higher level than that with captopril but lower than those with the other three peptides. The fluctuation of RMSD of peptide IV was most stable of the four peptides when coupled to ACE (Fig. 6b). Generally, the radius of gyration (R g) was used to describe the degree of unfolding of a protein to its native state (Wang et al., 2016). The variations in R g indicated that all four peptides and the captopril were able to loosen ACE after binding. The tightness of ACE changed least after binding with peptide I. Peptide II had the strongest effect on the unfolding of ACE and peptide IV had a similar effect with captopril (Fig. 6c). The average distance between the C-terminal carboxylic oxygen of each peptide and the zinc atom is shown in Fig. 6d. When the system of the dynamics process reached balance after 60 ns, the distance between the C-terminal carboxylic oxygen of peptide IV and the zinc atom was the longest of the four peptides followed by peptides II, III, and I in sequence. The root mean square fluctuation (RMSF) values reflect the fluctuations of individual residues; a lower RMSF value indicates greater stability (Ning et al., 2018). Both peptides and captopril can make ACE more flexible (Fig. 7). After binding, in regions 40‒204, 305‍‒‍390, 465‍‒‍485, captopril does not significantly change the RMSF value of ACE, but the value is evidently increased by four peptides at different time (Figs. 2 and 7).

Fig. 6. Stability of peptides‒angiotensin-converting enzyme (ACE) complex and the influence of peptides on ACE. (a) Root mean square deviation (RMSD) of ACE with and without peptides and captopril. (b) RMSD of peptides docked into ACE. (c) Radius of gyration (Rg) of ACE with and without inhibitors (peptides and captopril). (d) Average distance between the C-terminal carboxylic oxygen of each peptide and the zinc atom of ACE. RMSD, Rg, and distance in angstroms (1 Å=10 - 10 m) are plotted versus time. Molecular dynamic (MD) simulation was performed over 100 ns.

Fig. 6

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Fig. 7. Root mean square fluctuation (RMSF) of angiotensin-converting enzyme (ACE) with and without inhibitors (peptides and captopril) during molecular dynamic (MD) simulation.

Fig. 7

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4 Discussion

Food-derived ACE inhibitory peptides are diverse in source, sequence length, amino acid constitution, and IC50 values. Studies of the structure–activity relationship of ACE inhibitory peptides showed the presence of tyrosine (Y), phenylalanine (F), tryptophan (W), proline (P), lysine (K), isoleucine (I), valine (V), leucine (L), and arginine (R) in the peptides and amino acids with positive charges at the C-terminus exerting a strong influence on ACE binding (Daskaya-Dikmen et al., 2017). Kumar et al. (2015) identified 1745 antihypertensive peptides (AHTPs) from the literature and publicly available datasets and obtained random fragments from the Swiss-Prot proteins as a negative database to build a platform for predicting, screening, and designing AHTPs. They reported that residue P is highly abundant in AHTPs but amino acids such as aspartic acid (D) and serine (S) are less frequent in most AHTPs in comparison to non-AHTPs. In our study, peptides I, II, and IV all include P in their sequences, which may help to improve their inhibitory capacity. On the other hand, peptide III does not contain P but includes both D and S in its sequence, which may weaken its inhibitory potency. Using Kumar's platform (Kumar et al., 2015), the four peptides in our study obtained scores of 0.12, 0.24, -1.07, and 1.00, respectively. These results were consistent with their IC50 values obtained from the in vitro experiments. Thus, amino acid composition could have an important effect on antihypertensive potency. Moreover, judging from hydrogen bond occupancies for ACE–peptide complexes during the MD process, R of the four peptides all contribute most to the formation of hydrogen bonds for the stabilization of the complex (Table 2). This indicates that R is of great importance in the inhibition of ACE.

Most food-derived peptides inhibit ACE in a competitive or non-competitive manner (Puchalska et al., 2015). The peptides with competitive inhibition mode usually bind with the active site of ACE via hydrogen bonds (Chamata et al., 2020; Zheng et al., 2020), and even coordinate with Zn (II) of ACE (Tao ML et al., 2017). ACE has three main active site pockets: S1, S2, and S1'. S1 pocket includes Ala354, Glu384, and Tyr523 residues; S2 pocket includes Gln281, His353, Lys511, His513, and Tyr520 residues; and S1' includes Glu162 residue (Zheng et al., 2019). The result of molecular docking of captopril (a competitive positive control) in our study showed that captopril binds to the S2 pocket and forms hydrogen bonds with Tyr520 and Gln281. Peptides with a non-competitive inhibition pattern can work with or without the substrate present. Although it has been reported that a non-competitive inhibitory peptide YLVR could bind with certain amino acids in the ACE active site through cation–pi interactions (Liu et al., 2018), they usually cannot form hydrogen bonds with crucial amino acid residues in the active site of ACE (Zhao et al., 2019). As in our study, peptide III acted as a non-competitive inhibitor that does not bind the active sites of ACE. Peptides with competitive inhibition mode can have interactions with the active site pockets of ACE, but this does not mean that they certainly have better inhibitory activity. For example, ASL (IC50=102.15 μmol/L), AFKDETEEVPFR (IC50=80.20 μmol/L), and CRQNTLGHNTQTSIAQ (IC50=80.00 μmol/L) present competitive, non-competitive, and uncompetitive inhibition patterns, respectively. Among these three peptides, the competitive inhibitory peptide (ASL) does not show stronger inhibitory activity than the others (Tanzadehpanah et al., 2013; Wu et al., 2015; Forghani et al., 2016). Therefore, the ACE inhibitory potency of peptides in different inhibition modes cannot be directly compared.

As far as we know, only a few peptides are reported to exhibit uncompetitive inhibition, in which the peptide only binds to the enzyme–substrate complex and the inhibitor does not compete with the substrate, such as CRQNTLGHNTQTSIAQ from Stichopus horrens (Forghani et al., 2016) and FESNFNTQATNR from lysozyme hydrolysate (Asoodeh et al., 2012). Thus, the inhibition mechanism of uncompetitive inhibitory peptides has not been clearly illustrated. In our research, interestingly, peptides I, II, and IV all showed an uncompetitive mode of inhibition. The in vitro ACE inhibitory activity of peptide IV was higher than that of peptides II and I. The RMSD value of peptide IV was the lowest, which means that the stability of peptide IV was the highest, although the hydrogen bond number between ACE and peptide I was higher than that of peptide IV during MD simulation. In addition, the RMSF of ACE was enhanced most by peptide IV. The R g of peptide IV was higher than that of peptide I, which means peptide IV could significantly change the structure of ACE. Thus, our peptides may inhibit the activity of ACE by changing its conformation. After FAPGG (the ACE substrate) binds to ACE, it is hydrolysed to FAP. Our black tea peptides are located at the exit channel of the cavity where FAP is docked (Fig. 2). Our peptides with uncompetitive inhibitory mode may also block the dissociation of hydrolysis product of the substrate, and finally the hydrolysis will also act as an inhibitor of ACE. The inhibitory peptides with the same inhibiton mode could be affected by the distance between the C-terminal carboxylic oxygen of peptide and the zinc atom of ACE (Gunalan et al., 2020). In our study, results of MD simulation showed that the distance between peptide IV and the zinc atom of ACE is longer than those of peptides II and I.

In vitro inhibitory activity is a requirement for natural peptides to be a blood pressure-lowering material, and the in vivo biostability and bioavailability of peptides must also be considered. First of all, peptides should remain stable against gastrointestinal enzymes, because oral administration is considered to be the main intake route. Peptides could be digested by internal proteases in vivo and consequently alter their ACE inhibitory activity, compared to the ACE inhibitory activity of the peptides in vitro. For example, the ACE inhibitory activity of the peptide LVLPGELAK is enhanced twofold after simulated digestion, compared to the original value as in vitro(Dang et al., 2019). However, peptide YGGY (IC50=3.4 μmol/L) loses its inhibitory activity after simulated digestion (Saito et al., 1994). Some peptides could retain their activity after gastrointestinal enzyme digestion peptides such as RGQVIYVL, ASPKPSSA, and QFLLAGR isolated from quinoa branalbumin (Zheng et al., 2019) and SSYYPFK isolated from naked oat globulin (Zheng et al., 2020). It has been mentioned that the sequence and position of the amino acids of peptides determine their ability against gastrointestinal enzymes (Martin and Deussen, 2019). Sharma et al. (2014) developed a web sever "HLP" (http://crdd.osdd.net/raghava/hlp) to predict the half-life of peptides in an intestine-like environment and it can be used to predict the susceptibility of peptides towards protease degradation. The results analyzed by this server showed that peptides II, III, and IV presented high stability and peptide I presented normal stability. We noticed that peptide IV may be a substrate of trypsin. However, peptides containing Lys resisting gastrointestinal digestion have been reported bothin vitro and in vivo (García-Mora et al., 2017; Fan et al., 2019).

First, the small intestinal epithelium plays a crucial role in the absorption of nutrients and it is also a barrier against unfavourable substances in food. Second, peptides should be efficiently absorbed by the intestinal epithelium. Di- and tri-peptides are mainly transported via specific oligopeptide transporter proteins (peptide-transporters 1 (PEPT1)) of the absorptive cells (Shimizu, 1999; Groneberg et al., 2001). Paracellular pathways and transcytosis can help oligopeptides, with amino acid residues ranging from three to more, to transport (Miner-Williams et al., 2014). For example, the Caco-2 cell monolayer model is often used in simulated intestinal transport experiments in vitro. QIGLF (IC50=75.00 μmol/L) derived from egg white ovalbumin can be absorbed intactly through Caco-2 cell monolayers via a paracellular pathway (Ding et al., 2014). Peptide QAGLSPVR (IC50=68.35 μmol/L), which has proven clear ACE inhibitory potency in vitro and on spontaneously hypertensive rats, can transport through the Caco-2 cell monolayer via the paracellular pathway (Sun et al., 2019). However, two corn gluten-derived peptides YFCLT and GLLLPH transport across Caco-2 cell monolayers via both energy-dependent transcytosis and the paracellular pathway (Ding et al., 2018). A long-residue peptide (YQEPALGPVRGPFPIIV) from milk can transport through the Caco‐2 cell monolayer involved in transcytosis transport (Regazzo et al., 2010). So peptides in our study might be absorded via transcytosis or the paracellular pathway.

5 Conclusions

Food-derived multi-target peptides could be used as resources to explore the inhibition mechanisms of ACE inhibitory peptides. We examined four black tea peptides all located in a cavity away from the active site pocket of ACE. The peptides in this study were identified from black tea without any treatment of exogenous enzymes. The stability and bioavailability of black tea peptides in vivo still require further investigation. We endeavoured to find more bioactive peptides from tea in many different ways. The ACE inhibitory peptides discovered from black tea have potential for antihypertension and will contribute to a new conception of black tea consumption.

Acknowledgments

The research was supported by the National Key Research and Development Program of China (No. 2016YFD0200900) and the Science Technology Department of Zhejiang Province (No. 2016C02053-8), China.

Author contributions

Yating LU performed the experimental research and data analysis, and wrote and edited the manuscript. Yu WANG, Danyi HUANG, and Dongmei FAN performed the data analysis. Zhuang BIAN performed the experimental research. Peng LU reviewed and edited the manuscript. Xiaochang WANG supervised the research, and reviewed and edited the manuscript. All authors have read and approved the final manuscript and, therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.

Compliance with ethics guidelines

Yating LU, Yu WANG, Danyi HUANG, Zhuang BIAN, Peng LU, Dongmei FAN, and Xiaochang WANG declare that they have no conflict of interest.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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