A novel ACE inhibitory peptide from Pelodiscus sinensis Wiegmann meat

Pelodiscus sinensis meat is a nutritional food and tonic with angiotensin-converting enzyme (ACE) inhibitory activities. To identify the bioactive substances responsible, several bioinformatics methods were integrated to enable a virtual screening for bioactive peptides in proteins identified within a water-soluble protein fraction of Pelodiscus sinensis meat by Shotgun proteomics. The peptides were generated from the identified proteins by in silico proteolysis using six proteases. A comparison of the numbers of proteins suitable for digestion with each enzyme and the iBAQ (intensity-based absolute quantification) values for these proteins revealed that bromelain and papain were the most suitable proteases for this sample. Next, the water solubility, toxicity, and ADMET (absorption/ distribution/ metabolism/ excretion/ toxicity) properties of these peptides were evaluated in silico . Finally, a novel ACE inhibitory peptide IEWEF with an IC 50 value of 42.34 µM was identified. The activity of the synthesized peptide was verified in vitro , and it was shown to be a non-competitive ACE inhibitor. Molecular docking revealed that IEWEF could tightly bind to ACE, C-ACE, and N-ACE with energies of -8.9, -9.4, and -8.7 kJ·mol -1 , respectively. These results provide evidence that bioactive peptides in the water-soluble protein fraction account for (at least) some of the ACE inhibitory activities observed in Pelodiscus sinensis meat. Furthermore, our research provides a workflow for the efficient identification of novel ACE inhibitory peptides from complex protein mixtures.


Introduction
Hepatic fibrosis (HF) typically develops alongside the liver tissue repair and reconstruction processes that follow chronic liver injury, irrespective of its etiology.The cellular and molecular mechanisms underlying HF progression include parenchymal cell regeneration, interstitial cell activation, and extracellular matrix (ECM) deposition.Its importance is underlined by the finding that HF is the common pathological basis of most chronic liver diseases, and further deterioration may lead to liver cirrhosis or even liver cancer (Damiris et al., 2020).According to Global Cancer Statistics 2020, liver cancer is the second leading cause of death worldwide (Cao et al., 2020).Hence, prevention of HF progression and its treatment are an important research focus.
The renin-angiotensin system (RAS) also plays an important role in the occurrence and development of HF (Miranda and Simoes e Silva, 2017).Angiotensin II (Ang II), angiotensin converting enzyme (ACE), and Ang II type 1 receptor (AT1R) together constitute the ACE-Ang II-AT1R pathway, which promotes the development of HF.A second pathway comprised of angiotensin converting enzyme 2 (ACE2), Ang(l-7) metabolite (i.e., the product of ACE2 degradation of Ang II), and the Ang(l-7)-binding Mas receptor, which together constitute the ACE2-Ang(l-7)-Mas pathway, has a reverse regulatory effect on the ACE-Ang II-AT1R pathway (de Macêdoa et al., 2014).While ACE inhibitors such as captopril, lisinopril, and enalapril are known to regulate blood pressure by affecting the central RAS, they can also relieve organ fibrosis by affecting the local RAS (de Macêdoa et al., 2014).However, long-term use of these ACE inhibitors may elicit adverse reactions, such as dry cough, rash, and tachycardia (Ohishi et al., 2001), and occasionally serious adverse reactions such as renal dysfunction, increased risk of lung cancer, and systemic hypertension (Lin et al., 2020).Therefore, the search for novel ACE inhibitors for the safe treatment of these (and other) diseases is an important research objective.
The conventional strategy of searching for ACE inhibitory peptides in specific proteins is complex and time-consuming.In general, various purification technologies, e.g., ultrafiltration, gel chromatography, anion-exchange chromatography, or high performance liquid chromatography (HPLC), are applied in conjunction with activity evaluations to purify the active fractions (Chen et al., 2021).In recent years, bioinformatics methods have been increasingly utilized for the screening of active peptides (Tian et al., 2022).Proteomics databases, such as UniProt (www.uniprot.org),PDB (www.rcsb.org),and BIOPEP-UWM (https://biochemia.uwm.edu.pl/biopep/start_biopep.php) can be used to obtain the amino acids sequences of a protein of interest (Arámburo-Gálvez et al., 2022;Tian et al., 2022).Peptide cutter (http://www.expasy.org/resources/peptidecutter)or the "Enzymes action" tab in BIOPEP-UWM can then be used to hydrolyze the protein in silico (Arámburo-Gálvez et al., 2022).
To evaluate the biological activities of the identified peptides, both Peptide Ranker (http://distilldeep.ucd.ie/PeptideRanker/) and the "Profiles of potential biological activity" tab in BIOPEP-UWM can be subsequently used (Lin et al., 2023;Tian et al., 2022).In addition, ADMETlab can be used to systematically predict the in vivo pharmacokinetic properties (including absorption, distribution, metabolism, excretion, and toxicity) of the active peptides, providing evaluations that are especially conducive to the preliminary screening of the peptides ( Dong et al., 2018;Lin et al., 2023).By combining the above bioinformatics tools, it is possible to greatly reduce the complexity of the workflow required for screening ACE inhibitory peptides.Moreover, these virtual screening procedures significantly decrease the cost of searching for active peptides while improving overall efficiency.
However, the virtual screenings reported to date have (for the most part) utilized the amino acids sequences of a few specific proteins found in raw materials of interest or have utilized the sequences of proteins containing previously identified peptides (Arámburo-Gálvez et al., 2022;Tian et al., 2022).In the rare examples where the whole protein was evaluated, the reported searches were not comprehensive and systematic.
Pelodiscus sinensis (Wiegmann) is an edible aquatic animal that is nutritionally rich in protein (Wang et al., 2021).It has been demonstrated in numerous reports that protein hydrolysates from different parts of Pelodiscus sinensis, including the shell, meat, and egg, exhibit ACE inhibitory activities (Liao et al., 2018;Liu et al., 2012;Pujiastuti et al., 2017).While several ACE inhibitory peptides have been identified from the shell and egg (Liao et al., 2018;Pujiastuti et al., 2017;Rawendra et al., 2013;Rawendra et al., 2014), none have thus far been identified from the meat.In our previous study, we optimized a process for the enzymatic hydrolysis of water-soluble protein isolated from Pelodiscus sinensis meat (WSPM).Using degree of hydrolysis and ACE inhibitory activity as indices, we chose papain as the most suitable protease for this process (Sun et al., 2022).In the present study, an in silico hydrolysis of all proteins in the WSPM fraction was performed on sequences downloaded from UniProt following their identification from whole proteomic data obtained using Shotgun technology.Again, papain was chosen as one of the most suitable proteases, and this was partly consistent with our previous research (Sun et al., 2022).To screen out the ACE inhibitory peptides from WSPM proteins, an array of bioinformatics tools were combined to produce an effective workflow.Following their identification in silico, the active peptides were synthesized to verify their ACE inhibitory activities.The inhibitory mechanisms of proven active peptides were also analyzed by enzyme inhibition kinetics and molecular docking.In summary, we report herein the development of a strategy to screen protein hydrolysates using the most suitable protease, and we have used this strategy to reveal peptides with ACE inhibitory activities in Pelodiscus sinensis meat, thus laying a foundation for the further development of Pelodiscus sinensis meat protein products.

Materials and chemicals
WSPM prepared in our previous work (Sun et al., 2022)

LC-MS/MS analysis
LC-MS/MS analyses were performed on a Q Exactive mass spectrometer (Thermo Scientific) coupled to an Easy nLC (Thermo Fisher Scientific).First, the peptide hydrolysates were loaded onto a reverse phase trap column (Thermo Scientific Acclaim PepMap100, 100 µm × 2 cm, 5 µm) connected to a C18-reversed phase analytical column (Thermo Scientific Easy Column, 75 µm × 10 cm, 3 µm) equilibrated in buffer A (0.1% formic acid).The peptides were then separated using a linear gradient of buffer B (84% acetonitrile and 0.1% formic acid) at a flow rate of 300 nL/min (controlled by IntelliFlow technology) over a 120 min run.
The mass spectrometer was operated in positive ion mode.All MS data were acquired using a data-dependent top10 method for dynamically choosing the most abundant precursor ions from the survey scan (300-1800 m/z) for HCD fragmentation.The Automatic gain control (AGC) target was set to 3e6, and the maximum inject time to 10 ms.The Dynamic exclusion duration was 40.0 s.Survey scans were acquired at a resolution of 70 000 at m/z 200 and the resolution for HCD spectra was set to 17 500 at m/z 200, with an isolation width of 2 m/z.The Normalized collision energy was 27 eV, and the underfillratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%.The instrument was run with peptide recognition mode enabled.

Identification and quantitation of proteins
The MS raw data for each sample were combined and searched using MaxQuant software 1.5.3.17(Max Planck Institute of Biochemistry, Martinsried, Germany) against a Pelodiscus sinensis database.The parameter settings were as follows: (1) the enzyme used was trypsin and up to two missing cleavages were allowed; (2) fixed modifications was set to carbamidomethyl (C) and variable modifications was set to oxidation (M); (3) only proteins meeting a false discovery rate (FDR) ≤ 0.01 were classified as successfully identified; (4) protein quantification was calculated by the MaxQuant software using intensity-based absolute quantification (iBAQ), which provides an approximation to the absolute concentration of the protein in the sample.
In silico hydrolysis of WSPM The amino acids sequences of 49 different proteins identified by Shotgun analysis were obtained from the UniProt database (https://www.uniprot.org/)(Jul. 19, 2023).
The enzymes chymotrypsin (EC 3.4.21.1), trypsin (EC 3.4.21.4) . 19, 2023).For each protein, the frequency of release of fragments with ACE inhibitory activity by selected enzymes (AE), the relative frequency of release of fragments with ACE inhibitory activity by selected enzymes (W), and the theoretical degree of hydrolysis (DHt) were obtained by in silico hydrolysis.The most suitable protein hydrolysates and their associated enzymes were selected by comparing AE and W values.By comparing the numbers of proteins suitable for digestion with each enzyme and the iBAQ values of these proteins, the optimal enzyme for in silico hydrolysis of WSPM was selected.
In silico screening of ACE inhibitory peptides WSPM was hydrolyzed in silico using the optimal enzyme, and the peptides obtained were further screened in silico.In the first instance, the potential bioactivities of the peptides were predicted using the server PeptideRanker (http://distilldeep.ucd.ie/PeptideRanker/)(Jul.20, 2022), and previously unreported peptides with a score ≥ 0.5 were selected for further analysis.The Innovagen tool (http://innovagen.com/proteomics-tools)(Jul.21, 2022) was then used to predict the water solubility of these peptides, and peptides with "Good water solubility" were selected for the prediction of ACE inhibitory activities.The A value obtained from the "calculations" tab in BIOPEP-UWM program, which represents the frequency of fragments with a certain bioactivity (chosen from a toolbar) in a protein sequence or a specific peptide, was then used to predict the potential ACE inhibitory activities of the peptides.In addition, the ADMET properties (https://admet.scbdd.com/calcpre/index/),including HIA (human intestinal absorption) and BBB (blood-brain barrier) properties, were also evaluated (Jul. 22, 2023).Finally, all potentially bioactive peptides exhibiting HIA + and BBB + properties were screened for toxicity using the ToxinPred tool (http://crdd.osdd.net/raghava/toxinpred/design.php)(Jul. 22, 2023).

In silico analysis of the stability of ACE inhibitory peptides after the gastrointestinal (GI) digestion
To evaluate the stability of the ACE inhibitory peptides in vivo, the "enzyme action" tool in BIOPEP-UWM (http://www.uwm.edu.pl/biochemia/index.php/pl/biopep) (Jul.19, 2023) was applied again to predict the potential cleavage sites of the pepsin (pH 1.3), trypsin and chymotrypsin (A and C).

Peptide synthesis
Novel non-toxic peptides exhibiting a PeptideRanker score > 0.5, good water solubility, potential ACE inhibitory activity, and acceptable ADMET properties were chosen for in vitro evaluation of ACE inhibitory activity.The retrieved sequences were employed to synthesize peptides with a purity of 98% using the solid-phase synthesis procedure (GL Biochem Co., Ltd, Shanghai, China).Finally, the purity and the amino acid sequences of the synthesized peptides were determined by HPLC (Chuangxintongheng Science and Technology Co., Ltd, Beijing, China) and LC-MS (Shimadzu LCMS-2020, Kyoto, Japan), respectively.
In vitro assay of ACE inhibitory activities of the selected peptides ACE inhibitory activity was evaluated in vitro according to previous methods with some modifications (Liao et al., 2018;Xie et al., 2022).All of the assays were carried out in 0.1 mol/L sodium borate buffer (containing 0.3 mol/L NaCl at pH 8.3).For each peptide of interest, 40 µL ACE solution (100 mU/mL) was pre-incubated with the peptide (60 µL) at 37°C for 10 min.Next, 40 µL HHL solution (5.40 mmol/L) was added to the solution at 37°C, and the reaction was allowed to proceed for 15 min at 37°C.Finally, 60 µL 1 mol/L HCl was added to terminate the reaction.A borate buffer only control processed using the above procedure was also included for use as a blank.To quantify the amount of HA (hippuric acid) generated, 20 µL of reaction solution (pre-filtered using a 0.45 µm microporous membrane) was analyzed by HPLC (Waters e2695, Milford, USA) on a C18 column (150 × 4.6 mm, 5 µm; ThermoFisher Scientific, USA).The run conditions were as follows: mobile phase, methanol/ water/ phosphoric acid solution (15:84.9:0.1,V/V/V); flow rate, 1.0 mL/min; UV detection wavelength, 228 nm.All experiments were conducted in triplicate.ACE inhibitory activity was calculated according to the following equation: Eq.1 where A represents the peak area of HA in the sample, and B represents the peak area of HA in the blank.

Molecular docking
The

Statistical analysis
All experiments were performed in triplicate, and the data are reported as mean ± standard deviation (SD).SPSS 20.0 software (SPSS Inc., Chicago IL, USA) and GraphPad Prism 9.0 (GraphPad Software Inc., San Diego, CA) were used for the statistical analysis of the data.
Comparisons with a p < 0.05 were considered as statistically significant.

Results and Discussion
Identification of proteins from WSPM Shotgun proteomics is a useful tool for the rapid and direct analysis of complex protein mixtures (Wu et al., 2002).Moreover, this technique is capable of generating whole profiles for these proteins.In the egg of Pelodiscus sinensis, proteins belonging to nine protein families have been identified (Qiu et al., 2021).In the present study, we report a shotgun proteomics analysis of WSPM.To our knowledge, this is the first time shotgun proteomics has been applied to these or similar samples.As presented in Table 1, a total of 39 proteins were identified from WSPM using this approach.The identified proteins belong (in the main) to the following protein families: immunoregulation-related protein (phosphopyruvate hydratase, SH3 domain-binding glutamic acid-rich protein, inducible heat shock protein 70), malic enzyme, tropomyosin, myosin, antioxidant enzyme (Peroxiredoxin), actin, kinase, troponin, galectin, ATPase (Cation-transporting P-type ATPase C-terminal domain-containing protein), G-protein coupled receptors, glycolytic enzymes (Triosephosphate isomerase), calmodulin, parvalbumin, titin, vinculin, nucleolin, transferrin, nebulin, and myomesin.
In silico hydrolysis of WSPM The peptides released from parent proteins typically exhibit different activities depending on the enzymes used (Carrera et al., 2020).In virtual enzyme digestions, poorly-performing enzymes can be rejected from a screening based on the activity of the hydrolysate or on DHt (Rawendra et al., 2013;Sun et al., 2022).However, this process is time-consuming and essentially blinded.As a consequence, in silico enzyme digestion is now more and more applied to known proteins to eliminate guesswork and to predict the release of peptides accurately and rapidly (Hakimi et al., 2022).In silico digestion of the given proteins by the specific protease, like pepsin and trypsin, is the usual workflow (Arámburo-Gálvez et al., 2022;Carrera et al., 2020;Chen et al., 2022;Hakimi et al., 2022;Kartal et al., 2020).However, there has been no research correlated with the selection of the optimal protease in the simulated digestion.
In the present study, the 39 WSPM proteins identified using shotgun proteomics were each digested in silico with each one of the six common proteases (chymotrypsin, trypsin, pepsin, papain, bromelain, and alkalase).The most suitable protease for each protein was then determined by comparing AE and W values (see Table S1).The higher the AE and W values, the higher the activity of the in silico digestion products, and the more suitable the protease for that protein.Using this procedure, the optimal protease for each protein was selected (selections marked in red in Table S1).For one previously uncharacterized protein (Protein Accession: K7G5E5), the AE and W values were identical when papain and bromelain were used for the in silico digestion.However, the DHt values for these enzymes were observed to be different.As discussed above, DHt reflects the efficiency with which the enzyme produces peptides from the protein, and the higher the DHt value, the broader the specificity of the protease (Iwaniak et al., 2020).On this basis, bromelain (which exhibited a higher DHt value) was chosen as the optimal protease for the uncharacterized protein (Protein Accession: K7G5E5).It should also be noted that in silico digestion is not possible for proteins like titin (Protein Accession: K7G060), because the length (34915 amino acids residues) of the protein exceeds the capacity limit for the server.
The total number of proteins most suitable for in silico digestion by each protease and the iBAQ values of their respective proteins were calculated and are listed in Table 2. Papain was suitable for the in silico digestion of 13 proteins, with iBAQ values covering 48.33% of the whole protein.Bromelain was the optimal enzyme for 25 proteins, with iBAQ values covering 51.53% of the whole protein.The iBAQ value of titin, which could not be digested using in silico methods, accounted for only 0.14% of the whole protein.The above results suggest that papain and bromelain can both be considered suitable for the virtual digestion of WSPM, which is partly in agreement with our actual protease screen (Sun et al., 2022).
In total, 5670 peptides were released during the in silico digestion of 38 proteins by papain (Table S2); K7G2Y3 exhibited the richest abundance of fragments (894), while K7FM18 exhibited the fewest ( 22).All the released peptides were subsequently screened using the PeptideRanker program, yielding 1217 peptides with scores > 0.5.After secondary screening for the identification and removal of repetitive peptides and previously reported peptides, 663 peptides were evaluated for water solubility using the online Innovagen tool.In total, 318 peptides exhibited good water solubility, and these were evaluated in silico for ACE inhibitory activity.Analysis using the BIOPEP-UWM program identified 263 peptides that exhibited an A value indicative of ACE inhibitory activity.The ADMET properties, including BBB and HIA, of these 263 peptides were subsequently evaluated using ADMETlab.Only peptide IEWEF exhibited both BBB + and HIA + properties which was from myosin-1B-like.A subsequent evaluation of toxicity revealed that this peptide was "Non-toxic''.
In parallel, we performed an in silico digestion of proteins by bromelain.In total, 8508 peptides were released during the in silico digestion of 38 proteins by bromelain (Table S3); K7G2Y3 exhibited the richest abundance of fragments (1633), while K7FM18 exhibited the fewest ( 22).Subsequent screening using the PeptideRanker program yielded 1710 peptides with scores > 0.5.After secondary screening, 522 unique, unknown peptides were further evaluated for water solubility using the online Innovagen tool.In total, 268 peptides exhibited good water solubility, and these were evaluated in silico for ACE inhibitory activity.Analysis using the BIOPEP-UWM program identified 218 peptides that exhibited an A value indicative of ACE inhibitory activity.Again, after the prediction of ADMET properties, IEWEF was the only satisfactory peptide identified and it was from myosin-1B.
Although in silico digestion of WSPM by papain and bromelain released an abundance of peptides, only one peptide (IEWEF) met all of the necessary criteria.To facilitate in vitro testing, peptide IEWEF was synthesized using the solid-phase procedure, purified by HPLC, and subsequently verified by LC-MS.The HPLC and LC-MS chromatogram of IEWEF is shown in Figure S1.The purity of the synthesized peptide was 99.26%.

Prediction of the stability of ACE inhibitory peptides after the GI digestion
The peptide IEWEF would be cutted into IEW and EF by chymotrypsin A and cutted into IE, W, E, and F by chymotrypsin C. The peptide IEWEF was stable in the stomach, and was mainly digested in the intestinal tract.The potential products IEW, EF, and IE were all identified ACE inhibitory peptides possessing weaker activities (van Platerink et al., 2008;Jimsheena et al., 2010;Li and Aluko, 2010).Therefore, it could be speculated that the peptide IEWEF was not stable and may be digested into the products with decreased ACE inhibitory activities.The further in vivo studies of the stability of the peptide is still indispensable to verify the speculation.
The in vitro ACE inhibitory activities of IEWEF The IC50 value of peptide IEWEF against ACE was 42.34 ± 7.23 µM (Table S4).This is within the range (0.3~1000.0 µM) known to reduce blood pressure (Chen et al., 2022).There are several reported ACE inhibitory peptides similar to IEWEF in their amino acid compositions, including IE, EW, IEW, EF, LEF and IEEAF.The peptides IE and EW are both ACE inhibitory peptides isolated from milk hydrolysates, although their IC50 values have not been reported thus far (van Platerink et al., 2008).The peptide IEW from Arachin protein exhibits an IC50 value of 104 ± 3.0 µM (Jimsheena et al., 2010).The peptide EF is an ACE inhibitory peptide from pea protein with an IC50 value of 2980 ± 1240 µM (Li and Aluko, 2010).The peptide LEF containing the same C-terminal is also an ACE inhibiroy peptide from soybean and the IC50 value was 655.2 µM (Gu and Wu, 2013).Meanwhile, the peptide IEEAF possessing the similar N and C-terminal showed around 40% inhibitory activity against ACE at 0.5 mg/mL (Girgih et al., 2014).Compared with these peptides, our novel peptide IEWEF exhibits stronger ACE inhibitory activity.
An analysis of the structure-activity relationship between peptide amino acid composition and ACE inhibitory activities revealed that the content of hydrophobic amino acids residues was proportional to ACE inhibitory activity (Ding et al., 2021).The presence of terminal hydrophobic amino acid residues played an especially important role in ACE inhibitory activities (Ding et al., 2021)..
The content of hydrophobic amino acids residues in peptide IEWEF was 60%, and the N-terminal and C-terminal amino acids residues were both hydrophobic.Thus, peptide IEWEF exhibits the common core characteristics of ACE inhibitory peptides.Together, our results indicate that the novel peptide IEWEF exhibits strong ACE inhibitory activity.In addition, these results demonstrate that a workflow combing Shotgun analysis, in silico digestion and screening, and in vitro activity verification is a viable and effective strategy for the screening of novel ACE inhibitory peptides from a complex mixture of proteins.

Molecular docking analysis of IEWEF interaction with ACE
The molecular docking method is an important tool for exploring the interactions between peptides and ACE and for further elucidating the ACE inhibitory activity of peptides.The binding sites in ACE include three active pockets (S1, S2, and S1') and a zinc binding domain.The three active pockets include the following residues: the S1 pocket includes Ala354, Glu384, and Tyr523; the S2 pocket includes Gln281, His353, Lys511, His513, and Tyr520; and the S1' pocket includes the key residue Glu162 (Wei et al., 2022).Multiple different kinds of interactions have been observed between active peptides and ACE, including hydrogen bonds, hydrophobic interactions, van der Waals's force, π bonds, and electrostatic forces; with hydrogen bonding forming the strongest interactions (Wei et al., 2022;Kheeree et al., 2020).
For this study, 3D diagram representing the peptide-ACE complexes are shown in Figure 2 and  (Qi et al., 2018).While the two domains share 65% sequence similarity, they exhibit marked differences in their specificities for substrate, inhibitor, and chloride ions (Song et al., 2021).Thus, N-ACE hydrolyzes the anti-inflammatory peptide N-acetyl-SDKP, C-ACE is mainly responsible for the hydrolysis of angiotensin I, and bradykinin is hydrolyzed by both domains (Alfaro et al., 2020).With these characteristics in mind, domain selectivity has been the emphasis during the development of a new generation of ACE inhibitors (with reduced side effects).Song et al. studied the selective inhibition of several tyrosine-containing dipeptides by molecular docking, and the results achieved were consistent with those obtained from in vitro experiments (Song et al., 2021).Specifically, hydrogen bonding between the inhibitor and enzyme stabilized the binding complex, and the more hydrogen bonds formed, the stronger the affinity (Lin et al., 2019).
The interactions between peptide IEWEF and C/N-ACE are illustrated in Figure 3 and Table 3.
The peptide IEWEF formed only three hydrogen bonds to residues in the active S1 pocket of C-ACE (to Asn66 and to Asn70) (Alfaro et al., 2020).The other hydrogen bonds were to residues outside of the active pockets.The lower value of the binding energy for C-ACE indicates that IEWEF binds C-ACE more tightly (Duan et al., 2023) ] .

Conclusion
In the present paper, 39 proteins in Pelodiscus sinensis meat were identified by Shotgun analysis for the first time.In addition, an abundance of peptides released from these proteins by virtual digestion were identified and screened.By combining a series of virtual digestion and screening procedures, an efficient in silico screening platform for ACE inhibitory peptides was established.Using this platform, a novel ACE inhibitory peptide IEWEF (Ile-Glu-Trp-Glu-Phe) with an IC50 value of 42.34 µM was successfully screened out.An in vitro enzyme inhibition kinetics analysis of the synthesized peptide and a molecular docking study provide evidence that peptide IEWEF is a non-competitive ACE inhibitor, and that it mainly binds to ACE outside of the active pockets.The molecular docking study also revealed that peptide IEWEF binds to C-ACE more tightly than to N-ACE.
Our findings provide additional evidence for the presence of ACE inhibitory peptides in WSPM.
In addition, they demonstrate that a new workflow established in this study can be applied for the rapid and accurate identification of novel ACE inhibitory peptides from complex protein mixtures.Future research should include a clarification of the domain selectivity of IEWEF, an investigation of the in vivo anti-ACE activities of IEWEF, and a search for more novel ACE inhibitory peptides using the established workflow on other protein mixtures.

Statements and Declarations
Competing interests The authors declare no competing interests.

Conflict of interest
The authors declare no confict of interest.

Informed consent
crystal structures of human ACE bound with lisinopril (ID: 1O86, 1O8A, and 2C6N) were retrieved from the RCSB Protein Data Bank (https://www.rcsb.org)(Jul.25, 2023).While 1O86 contains the coordinates for the entire ACE molecule, 1O8A contains the coordinates for only the C domain of ACE (PDB ID:1O8A), and 2C6N contains the coordinates for only the N domain of ACE.All heteroatoms, water molecules, and lisinopril were removed (with the exception of zinc and chloride ions), and hydrogen atoms were added.The 2D structure of ACE inhibitory peptide was initially drawn using ChemBioDraw Ultra 20.0, and this was transformed into a 3D structure using ChemBio3D Ultra 20.0.The structures of ACE, C-ACE, N-ACE, and peptide were converted into PDBQT format using AutodockTools-1.5.6.AutoDock Vina was then used for the molecular docking procedure.The docking center of 1O86 was set at (x = 40.582,y = 37.177, z = 43.444),and the grid box size (spacing) was 0.592.The docking center of 1O8A was set at (x = 40.562,y = 37.388, z = 43.476),and the grid box size (spacing) was 0.547.The docking center of 2C6N was set at (x = -21.544,y = -21.229,z = -63.629),and the grid box size (spacing) was 0.913.The exhaustiveness value was 20.Finally, PyMOL was used to analyze interactions between the peptide and ACE.
To elucidate the ACE inhibition kinetics of peptides of interest, different concentrations of HHL (0.27 mM, 0.54 mM, 1.08 mM, and 2.16 mM) and different concentrations of peptide (0 µM, 69 µM, 138 µM) were prepared in sodium borate buffer.The ACE activities of these test solutions were then determined.A Lineweaver-Burk plot was applied to determine the inhibition mode.The maximum enzyme reaction rate (Vmax), Michaelis-Menten constant (Km), and inhibition constant (Ki) were calculated from these graphs.All experiments were repeated in triplicate.

Table 3 .
The peptide IEWEF bound ACE through two hydrogen bonds to Ala356 away from the ACE active sites.This finding is consistent with our kinetic study, which indicated that IEWEF interacts with the non-active domain of ACE.ACE has two known isoforms, testis ACE (tACE) and somatic ACE (sACE).sACE is composed of an N-terminal domain and a C-terminal domain (N-ACE and C-ACE) No informed consent is required for this study antioxidant and a-glucosidase inhibitory peptides identified from fermented rubing cheese through ://doi.org/10.1016/j.lwt.2022.113196Wu C. C., Maccoss M. J. (2002) Shotgun proteomics: Tools for the analysis of complex biological systems.Curr.Opin.Mol.Ther.4, 242-250.https://doi.org/10.1016/S0197-2456(02)00192-7Xie D. W., Du L., Lin H. S., Su E. Z., Shen Y. L., Xie J. L., and Wei D. Z. (2022) In vitro-in silico screening strategy and mechanism of angiotensin I-converting enzyme inhibitory peptides https

Table 2 .
The number and the iBAQ values of proteins most suitable for each protease

Table 3 .
The binding energy and interactions of IEWEF by molecular docking with ACE, C-ACE and N-ACE