A combined in vitro and in silico study of the inhibitory mechanism of angiotensin-converting enzyme with peanut peptides

3.0 科研~小助 2025-09-01 5 4 8.19MB 13 页 1知币
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International Journal of Biological Macromolecules 268 (2024) 131901
Available online 26 April 2024
0141-8130/© 2024 Elsevier B.V. All rights reserved.
A combined in vitro and in silico study of the inhibitory mechanism of
angiotensin-converting enzyme with peanut peptides
Jiale Liu
a
, Wentian Song
a
, Xue Gao
a
, Jiaoyan Sun
a
, Chunlei Liu
a
, Li Fang
a
, Ji Wang
a
,
Junhua Shi
a
, Yue Leng
a
, Xiaoting Liu
a
,
*
, Weihong Min
a
,
b
,
**
a
College of Food Science and Engineering, National Engineering Laboratory of Wheat and Corn Deep Processing, Jilin Agricultural University, Changchun 130118, Jilin,
China
b
College of Food and Health, Zhejiang A&F University, Hangzhou 311300, China
ARTICLE INFO
Keywords:
Angiotensin-converting enzyme
Inhibitor
Peanut peptides
Molecular dynamics simulation
HUVEC cell
ABSTRACT
Food-derived peptides with low molecular weight, high bioavailability, and good absorptivity have been
exploited as angiotensin-converting enzyme (ACE) inhibitors. In the present study, in-vitro inhibition kinetics of
peanut peptides, in silico screening, validation of ACE inhibitory activity, molecular dynamics (MD) simulations,
and HUVEC cells were performed to systematically identify the inhibitory mechanism of ACE interacting with
peanut peptides. The results indicate that FPHPP, FPHY, and FPHFD peptides have good thermal, pH, and
digestive stability. MD trajectories elucidate the dynamic correlation between peptides and ACE and verify the
specic binding interaction. Noteworthily, FPHPP is the best inhibitor with a strongest binding afnity and
signicantly increases NO, SOD production, and AT2R expression, and decreases ROS, MDA, ET-1 levels, ACE,
and AT1R accumulation in Ang II-injury HUVEC cells.
1. Introduction
Hypertension is a dramatic risk factor in cardiovascular diseases,
including strokes and coronary heart diseases [1], and the renin-
angiotensin-aldosterone system (RAAS) is the main regulator of blood
pressure [2]. In the system, angiotensin-converting enzyme (ACE) is a
zinc dipeptidyl carboxypeptidase, which plays a pivotal role in regu-
lating blood pressure and electrolyte homeostasis through RAAS [3].
Additionally, ACE catalyzes the cleavage of the C-terminal histidine-
leucine dipeptide from angiotensin I (Ang I) to produce angiotensin II
(Ang II, a potent vasoconstrictor octapeptide) [4]. Therefore, inhibiting
ACE activity is a successful strategy for preventing and remedying high
blood pressure.
Currently, a range of ACE-inhibiting synthetic drugs usually with a
sulfhydryl (captopril), carboxyl (perindoprilat/lisinopril/enalaprilat),
or ketone (keto-ACE) blocks for coordination of the zinc ion have been
routinely applied for hypertension treatment [5]. However, after long-
term treatment, these drugs often provoke various side effects, such as
fetopathy, angioedema, hyperkalemia, persistent dry cough, and kidney
problems [68]. Thus, a growing attention has been drawn in identifying
food-derived ACE inhibitory (ACEI) peptides which could control blood
pressure in a safe way. Up to now, ACEI peptides have been isolated from
various kinds of food, such as seafood [911], plants [1,1215], and
dairy products [1618].
It has been demonstrated that famous peptides Ile-Trp (IW), Val-Pro-
Pro (VPP), and Ile-Pro-Pro (IPP) from milk [19], Ile-Arg-Trp (IRW) [16]
and Arg-Val-Pro-Ser-Leu (RVPSL) from eggs [20], IAVPTGVA and LPYP
from soybeans [15], YLVR from hazelnuts [21] which are dissociated by
enzymatic hydrolysis of the parent protein, microbial fermentation, and
production of recombinant peptides [22] reduced activity of ACE and
decreased blood pressure. These innovative and conventional experi-
ment approaches induce many problems, such as extensive time and
cost, and irrecoverable function. Accordingly, computational techniques
including molecular docking and molecular dynamics (MD) simulations
[23] have been performed to screen ACEI peptides, characterize inhib-
itory activities, and modify original peptides for seeking better in-
hibitors in recent years. Although a number of ACEI compounds which
assimilate easily, from the intestinal tract into blood circulation pro-
moting antihypertensive effects [24] have been produced, there is very
limited information on the relationships between structure and activity
* Corresponding author.
** Correspondence to: W. Min, College of Food and Health, Zhejiang A&F University, Hangzhou 311300, China.
E-mail addresses: liuxiaoting@jlau.edu.cn (X. Liu), minwh2000@zafu.edu.cn (W. Min).
Contents lists available at ScienceDirect
International Journal of Biological Macromolecules
journal homepage: www.elsevier.com/locate/ijbiomac
https://doi.org/10.1016/j.ijbiomac.2024.131901
Received 8 January 2024; Received in revised form 6 April 2024; Accepted 24 April 2024
International Journal of Biological Macromolecules 268 (2024) 131901
2
(SAR) and even the specic inhibition mechanism between ACEI ligands
and the protein receptor [2527]. Therefore, the development of stra-
tegies quantitatively characterizing the functional mechanisms of ACE is
of paramount importance, which is prot for the discovery of novel
inhibitors with simplied production process and excellent
bioavailability.
In this study, we obtained peanut-derived ACEI peptides by database
screening, molecular docking, validation of inhibitory activity, MD
simulations, as well as HUVEC cells. In addition, employing in-vitro in-
hibition kinetics of ACEI peptides by high-performance liquid chroma-
tography (HPLC) and network modeling approaches, this work
systematically examined the inhibitory mechanism of ACE interacting
with peanut peptides. Our study also offers a useful perspective of
computational biology on the screening ACEI peptides and revealing in-
depth interaction by analyzing dynamic networks and the atomic level
interaction including cation-
π
interaction, metal coordination, van der
Waals, and hydrogen bonds between peptides and ACE residues.
2. Materials and methods
2.1. Materials
ACE from rabbit lung and N-Hippuryl-His-Leu hydrate (HHL) and 3-
(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)
were purchased from Sigma-Inc (St. Louis, MO, USA). Pepsin (3000 U/g)
and trypsin (250 U/g) was bought from Beijing Dingguo Biotechnology
Co., Ltd. (Beijing, China). Acetonitrile (HPLC grade) and triuoroacetic
acid (LC-MS grade) were obtained from Fisher Scientic (Pittsburgh, PA,
USA). Angiopoietin-2 Protein, Huma (10 mg) were from MedChemEx-
press (Shanghai, China). Human umbilical vein endothelial cells
(HUVEC) were procured from Zhongqiao Xinzhou Biotechnology Co.,
Ltd. (Shanghai, China). Fetal bovine serum, streptomycin and penicillin
were obtained from Gibco (New York, USA). NO and ET-1 ELISA kits
were bought from Biyuntian Reagent Company. Reactive oxygen species
(ROS), malondialdehyde (MDA), superoxide dismutase (SOD), and BCA
protein concentration kits were obtained from Nanjing Jian Cheng
Bioengineering Research Institute. Antibodies against ACE, AT1R,
AT2R, and β-Actin were from Abcam (Wuhan, China). Other reagents
were of analytical grade and commercially available.
2.2. In-silico screen of peanut ACEI peptides and selected peptide
synthesis
According to the structural features and characteristic amino acids of
ACE-inhibiting peptides, some peptides were screened from the peanut
peptide sequences previously prepared and characterized in our labo-
ratory. The potential biological activity of these peptide fragments was
scored on the Peptide Ranker website (http://distilldeep.ucd.ie/Pepti
deRanker/) [28]. Moreover, ToxinPred was a unique in-silico approach
to predict toxicity of ACE inhibitory peptides [29]. The screening pep-
tides were further screened for free binding energy by molecular dock-
ing using AutoDockZn [30]. The crystal structure for ACE was taken
from the protein databank (PDB code: 4AA1) [31]. All ligands and water
molecules were removed. The peptide with the most favorable binding
energy (increasingly negative value) was considered as a potential
inhibitor.
The peanut peptides chosen were synthesized by Jiangsu Jitai Pep-
tide Industry Technology Co., Ltd. (Yancheng, China) using standard
solid-phase peptide synthesis and their purities and molecular weights
were identied by HPLC and LC-MS analysis separately.
2.3. ACE inhibition assay
ACEI activities were measured based on the method [32]. Briey, 50
μ
L of sample solution (samples were placed in 50 mM borate buffer
solution containing 0.3 M NaCl, pH 8.3) was incubated with 50
μ
L of
ACE (0.25 U/mL) at 37 C for 10 min. The mixture was subsequently
incubated with 150
μ
L of HHL (4.15 mM HHL in 50 mM borate buffer)
for 1 h at 37 C. The reaction was terminated by adding 250
μ
L of 1 M
HCl to the experimental samples, and the blank control group was added
with HCl and incubated for 1 h before adding the substrate. Then, add
500
μ
L of ethyl acetate, shake for 15 s to extract hippuric acid, centrifuge
at 3000 r/min for 10 min, then collect 200
μ
L of supernatant, and
evaporate in an oven at 110 C. The residue was dissolved in 1.0 mL
distilled water, and the absorbance of the solution was measured at 228
nm. ACE inhibitory activity was calculated by the following equation:
ACE inhibition activity (%) = [(CA)/(CB) ] × 100
where C is the absorbance without the addition of the sample solution, A
is the absorbance of the presence of ACE with the sample solution, and B
is the absorbance of the blank group (HCl was added to inhibit reaction
before adding HHL substrate).
2.4. ACE inhibitory pattern with different peptides
To explore the inhibition mechanisms, we monitored the effects of
different concentrations of HHL substrate (0.83, 1.38, 2.75, 4.15 mM)
and used different concentrations (0, 1.0, 2.5
μ
M) of peanut peptides.
The reaction mixture also contained different concentrations of peptides
and 0.125 U/g ACE in 50 mM borate buffer. The mixture was incubated
at 37 C for 10 min, and absorbance was monitored at 228 nm. The
pattern of ACE inhibition was determined by Lineweaver-Burk in Origin.
The inhibition constants (K
i
) were investigated by interpretation of the
Dixon plots, in which the value of the x axis infers -K
i
[21].
2.5. Stability of synthetic peptides
The stability of peptides was validated by the previously described
method [33] with minor modications. The synthetic peptides were
prepared as solution with pH of 7.0 and concentration of 100
μ
M, be-
sides as a blank control. The samples were water-bathed for 2 h at
different temperatures and pH to determine thermal and pH stability.
100
μ
M peptide solution at pH 2 was added with a certain mass of
pepsin (1:3000) and in a water bath at 37 C for 1 h. After sampling, pH
was adjusted to 7. Then an appropriate amount of trypsin (1:250) was
added and digested in a water bath at 37 C for 1 h. After inactivated in
100 C water bath for 10 min, it was ltered with 0.22
μ
M lter mem-
brane and analyzed by RP-HPLC. The degradation of the synthesized
peptides under different conditions was determined based on the peak
areas and peak emergence times of the liquid phase proles.
2.6. Molecular dynamics simulations
All-atom MD simulation via NAMD package [34] was carried out
based on molecular docking results. The protonation state of each his-
tidine residue was determined according to their respective chemical
environments. TIP3P water model, incorporated by 154 mM NaCl, was
used to imitate the solvent environment. Topology and force eld pa-
rameters were assigned from the CHARMM36 protein parameter set
[35]. In each MD simulation, periodic boundary conditions were utilized
to avoid boundary effect caused by nite size. The van der Waals and
short-range electrostatic interactions were gradually reduced to 0 in the
range of 10 to 12 Å. The particle mesh Ewald method was employed to
compute long-range electrostatic interactions [36]. The simulation sys-
tems were rst subjected to 50,000 steps of energy minimization with
the protein heavy atoms and ligands backbone atoms harmonically
constrained by a force constant of 0.5 kcal/mol Å
2
. Following the
minimization, at the same restraint, 5 ns additional MD simulation in the
canonical (NVT) ensemble were implemented to ensure the temperature
stability by Langevin thermostat [37]. Thereafter, 200 ns MD simula-
tions in the NPT ensemble were run to adequately sample the
J. Liu et al.
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