Chang Lu^a*, Ting Wu^1,b, Xiaoyun Xu^1,c

¹Department of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, P. R. China

^aCorresponding E-mail: 15231078009@163.com

^bE-mail: ting.wu@mail.hzau.edu.cn

^cE-mail: xuxiaoyun@mail.hzau.edu.cn

Abstract

The aim of this study was to prepare Grape seed protein peptide (GSPP) with direct antioxidant activity from wine grape seeds using a three-step method. The optimal enzyme addition rate (190 U/mg), enzymatic temperature (52℃), and enzyme digestion temperature (52℃) for the preparation of grape seed protein peptide (GSPP) by alkaline 说protease were determined by Box Behnken experimental design. An oxidative stress model was established by bisphenol S (BPS) induced RAW 264.7 in mouse monocyte macrophages to evaluate the indirect antioxidant activity of GSPP in terms of ROS, T-AOC, MDA, and GSH. Peptide sequences were analyzed using LC-MS/MS, and peptides with anti-inflammatory activity potential were screened and synthesized based on database prediction, network pharmacology, and molecular docking. The eight synthesized peptides, WGF, SHFGF, EGPFF, WAPR, SFYRAF, HFAFL, PGRF, and FDSF, significantly reduced the secretion of NO, TNF-α, and IL-6 at a concentration of 10 μg/mL (p < 0.05) and inhibited the expression of p65, Tnfα, Il6, Il1b and Nos2 mRNA in the inflammatory pathway, suggesting that they may exert anti-inflammatory activity through the modulation of the nuclear transcription factor κB (NF-κB) signaling pathway. These findings suggest that GSPP has good potential for antioxidant and anti-inflammatory properties and is expected to be developed as a natural antioxidant and anti-inflammatory agent.

Keywords: grape seeds; active peptides; oxidative stress; anti-inflammatory activity

1. Introduction

In the context of the expanding global population and mounting emphasis on dietary health, the acquisition of protein from plant sources has emerged as a pivotal strategy for diversifying food resources in a multifaceted manner. This development holds immense significance for fostering the sustainable growth of the food industry and ensuring human health[1]. The wine industry, a major agro-processing industry around the world, generates a massive amount of winemaking byproducts each year, principally grape seeds, skins, and stalks[2]. Of these, grape seeds account for 38%-52% of the total dry weight of by-products, with protein content ranging from 8% to 14% [3]. The wine industry generates millions of tons of fermented grape seed by-products annually, which are rich in protein peptides with both resource recovery potential and bioactive properties [4]. As an important resource by-product, grape seed has the potential to be widely used in food, health care, cosmetics, chemical industry and other industries But a large amount of fermented grape seeds are disposed of as waste every year, which causes a great waste of protein resources and puts great pressure on the environment [5].

Grape seeds contain proteins, polyphenols, and flavonoids, but their utilization is predominantly focused on proanthocyanidins and oils, with limited attention to protein-derived peptides [6]. Traditional protein extraction methods (e.g., alkali solubilization and acid precipitation) face challenges such as phenolic interference and organic solvent residues, which compromise protein quality [7]. Enzymatic hydrolysis, a mild and controllable process, is widely adopted for peptide production. However, the relationship between enzymatic specificity (e.g., cleavage sites for aromatic or hydrophobic amino acids) and the bioactivity of peptides requires further elucidation. Previous studies have indicated that enzymatic hydrolysis of grape seed protein yields grape seed protein peptides with beneficial bioactivity. These peptides display significant antioxidant bioactivity, effectively inhibiting lipid oxidation, scavenging free radicals, and reducing ferric ions.

Oxidative stress and chronic inflammation are interconnected pathological processes underlying cardiovascular diseases, neurodegenerative disorders, and cancer [8]. The excessive production of ROS activates the NF-κB signaling pathway, leading to the release of pro-inflammatory cytokines (TNF-α, IL-6, IL-1β) and perpetuating a vicious cycle of inflammation and oxidative damage [9, 10]. Current therapeutic strategies, such as corticosteroids and non-steroidal anti-inflammatory drugs, often cause gastrointestinal and hepatic side effects with long-term use [11]. Bioactive peptides, with their low molecular weight and high target specificity, offer a safer alternative for treating diseases such as oxidative stress and inflammation [12].

In this work, taking grape seeds from winemaking by-products as the research object, orthogonal test and response surface test were used to optimize the preparation process of GSPP, and bioinformatics method was used to screen out the biologically active protein peptides, and in vitro free radical scavenging experiments and the construction of RAW 264.7 cell model were used to validate the effects of GSPP on oxidative stress and inflammation in order to provide theoretical basis and technological support for the high-value utilization of the by-products of grape processing and lay the foundation for the development of natural antioxidant and anti-inflammatory preparations. It provides theoretical basis and technical support for the utilization of grape processing by-products, and lays the foundation for the development of natural antioxidant and anti-inflammatory preparations.

2. Materials and Methods

2.1 Materials and Reagents

Grape seeds wre separated from MC2 grape pomace provided by Shandong Agricultural University. Alkaline protease, papain, bromelain, pepsin and trypsin were purchased from Aladdin. All other chemical reagents are analytically pure and were purchased from Sinopharm Chemical Reagent Co. Commercial kits for measuring the levels of ROS, T-AOC, MDA, GSH, NO, TNF-α, and IL-6 in cells were purchased from Jiancheng Bioengineering Institute (Nanjing, China).

2.2 Dephenolization, protein extraction, peptide preparation, and process optimization

Grape seeds were separated from grape pomace after winemaking, rinsed with water to remove the residual cellar mud and pomace powder, and naturally air-dried until the moisture content was less than 10%. Weigh a certain amount of grape seeds, add 0.05 mol/L NaOH according to the material-liquid ratio of 1:10 (w/v), put in a water bath at 80℃ and stirring leaching for 20 min, repeated twice. Rinse the grape seeds with water to remove the softened and shed grape seed shells, dry and pulverize, and pulverized grape seed powder was sieved through a 60-mesh sieve to obtain the de-phenolized grape seed powder.

The grape seed meal protein was isolated via alkaline extraction and subsequent isoelectric precipitation, a widely employed technique for protein extraction documented in the scientific literature [13, 14]. In the procedure, dephenolized grape seed powder was extracted with NaOH under controlled temperature, time, pH, and solid-liquid ratio conditions. Following protein extraction, the extract was centrifuged at 4000 r/min for 15 min. The pH of the supernatant was adjusted to the isoelectric point (pI) of grape seed protein using HCl, and the mixture was centrifuged at 4000 r/min for 15 min, discarding the supernatant and retaining the precipitate. The pH of the precipitate solution was adjusted to neutral with 0.1 mol/mL NaOH, followed by centrifugation at 4000 r/min for 15 min, discarding the supernatant and retaining the precipitate. The grape seed protein slurry was obtained by washing the precipitate with distilled water multiple times. The grape seed protein slurry was then freeze-dried using a vacuum freeze-dryer, yielding the final grape seed protein product. Orthogonal experiment L9 (3⁴) was used to optimize the extraction process conditions of grape seed protein. The variables and their levels were studied as follows: temperature (A) from 40 to 50 ℃, time (B) from 60 to 120 minutes, pH (C) from 9.0 to 11.0, and solid-liquid ratio (D) from 1:20 to 1:30(Table 1). Each experiment is randomly repeated three times or more.

Table 1. Orthogonal experimental factors and levels for extracting grape seed protein.

Independent Variables	Coded Symbols	Coded Factor Levels
		1	2	3
Temperature（℃）	A	40	45	50
Time（min）	B	60	90	120
Ph	C	9.0	10.0	11.0
Material liquid ratio（min）	D	1:20	1:25	1:30

Based on protease specificity, the appropriate enzyme for preparing GSPP was screened among five proteases: papain, alcalase, bromelain, pepsin, and trypsin. Response surface methodology was employed to evaluate the effect of different variables on the antioxidant activity of GSPP. After determining the variables and their preliminary ranges through literature review and preliminary experiments, a Box-Behnken Design with three independent variables at three levels was employed to optimize the preparation conditions of GSPP, using the radical scavenging activity of the protein hydrolysate as the response variable. The studied variables and their corresponding levels are summarized in Table 2. According to the experimental design, a total of 19 experimental runs were conducted in triplicate under randomized order.

Table 2. Variables and levels used for central composite design.

Independent Variables	Coded Symbols	Coded Factor Levels
		-1	0	1
Enzyme concentration（U/mg）	A	80	120	160
Temperature（℃）	B	35	45	55
Time（min）	C	60	90	120

2.3 Establishment of a Bisphenol S (BPS)-induced RAW 264.7 cell model to investigate the antioxidant and anti-inflammatory activities of GSPP

RAW 264.7 cells, capable of simulating phagocytosis and clearance of foreign substances in vivo, are also responsive to external stimuli, secreting reactive oxygen species (ROS) and inflammatory mediators (e.g., TNF-α, IL-6), thus making them suitable for studying oxidative stress and inflammatory activity. BPS, a common component of food packaging, can liners, and thermal paper, has been shown to migrate into the food chain, posing a potential health risk. Employing BPS as an inducer in RAW 264.7 cells can trigger both oxidative stress and inflammatory responses [15], thereby closely mirroring the pathological mechanisms of actual exposure.

RAW 264.7 cells with good growth condition and in logarithmic growth phase were taken, and the cell concentration was adjusted to 1×10⁶ cells/mL, added to 12-well cell culture plates, and cultured in a cellular thermostat incubator at 37℃ with 5% CO₂ for 12 h. The cells were added with different concentrations of GSPP solution for 4 h, and then stimulated with 10 ^-7 mol/L BPS for 6 h. The same volume of DMEM basal medium was used as a blank control. DMEM basal medium as blank control. The cell culture fluid was collected, and the supernatant was centrifuged to determine the content of oxidative stress and inflammatory response products according to the kit method.

2.4 Identification of bioactive peptides from grape seed protein hydrolysate (GSPP) using a bioinformatics-based approach

LC-MS/MS was used to identify peptide sequences in GSPP. Analyze the biological activity scores of oligopeptides in the peptide library through the Peptide Ranker database (http://distilldeep.ucd.ie/Peptide Ranker/), and screen out peptide segments with activity scores ≥ 0.9; Determine whether the peptide segment is toxic through Toxin Pred (http://toxinpred2.imtech.res.in/). Predicting free radical scavenging ability using PepQSAR (http://i.uestc.edu.cn/PQsarDB/) and Multipep (https://agbg.shinyapps.io/MultiPep/), and predicting anti-inflammatory activity using PreAIP (http://kurata14.bio.kyutech.ac.jp/PreAIP/).

Compound targets were predicted using SwissTargetPrediction. In order to predict the target of a compound, the GeneCard and OMIM databases were searched for relevant disease targets using the keyword “inflammation”, and duplicates were eliminated. Venny 2.1.0 was used to obtain the intersection target of the compounds and the disease. After being imported into the STRING database, the protein-protein interaction network could be obtained, and the degree of each node was calculated.The intersecting targets of the compounds and the disease were visualized by KEGG pathway annotation analysis and GO functional enrichment analysis using Metascape.

The three-dimensional structure of peptides was constructed using ChemDraw 23.1.1 and Chem3D 23.1.1 software and subjected to energy minimization processing. The crystal structure of RELA (p65) (PDB ID: 1MY5) was obtained from the Protein Data Bank (https://www.rcsb.org/) PyMol 2.5.4 software was used to obtain and remove ligand. The RELA and peptides were opened in Autodock Tools 1.5.7 software, after water removal and hydrogenation treatment, a semi-flexible docking method was adopted for docking. PyMol 2.5.4 software for three-dimensional visual analysis was used.

2.5 Statistical Analysis

All experiments were repeated three times and more, and the results were expressed as mean ± standard deviation (SD). All data obtained from the experiments were analyzed using IBM SPSS Statistic Version 25.0 for statistical software, and data from different groups were analyzed by ANOVA one-way analysis of variance significance analysis using t-test, and plotted using GraphPad Prism 9.5 software. p < 0.05 indicated statistical significance.

3. Results and discussion

3.1 The dephenolization effect of grape seeds

The polyphenol content of grape seeds before and after dephenolization is shown in Figure 1. The total phenol content of grape seeds was 33.78 ± 3.68 mg (GAE)·g, and the total phenol content of grape seeds after dephenolization by weak alkaline leaching was 1.34 ± 0.32 mg (GAE)·g, and the phenolics removal rate reached 96.00%, which was significantly lower than that of the chemical method of dephenolizing grape seeds using organic reagents, such as methanol, hexane, etc. (p < 0.05). The polyphenol content in the seeds (2.72 ± 0.08 mg (GAE)·g) was significantly lower in this method than in the chemical method of phenol removal using methanol, n-hexane and other organic reagents (p < 0.05).

Figure 1. Phenolic content in grape seeds processed with different dephenolization methods.

Since phenolics such as anthocyanins and proanthocyanidins have a significant effect on the color of grape seed protein[16], and phenolics produce bitter and astringent taste, which affects the organoleptic quality of grape seed proteins and protein peptides, in addition, polyphenols and proteins may combine to form insoluble complexes, which reduces the bioavailability of grape seed protein peptides[17]. As shown in Figure 2, the protein extracted from grape seed before dephenolization was darker in color, showing obvious brownish-red, while the grape seed protein after dephenolization was lighter beige, and the difference in protein color could indicate that the weak alkaline leaching method has a better effect on the removal of phenolics from grape seed, which provides favorable conditions for the application of grape seed protein and protein peptide in food and other fields in the later stage.

(A)

(B)

Figure 2. Lyophilized grape seed protein powder before (A) and after (B) dephenolization treatment.

3.2 Analysis of Orthogonal Experiment Results for Grape Seed Protein Extraction

Based on the orthogonal experimental results (Table 3), the order of factors influencing protein extraction was determined as C>A>D>B. The extraction pH (factor C) exhibited the most significant effect on protein yield (p < 0.01), which is due to the pH-dependent solubility changes in plant protein isolation processes [18]. Extraction temperature (factor A) ranked second, which may be attributed to the dual effects of thermal disruption of cell walls and potential protein denaturation. Although material -liquid ratio (factor D) and extraction time (factor B) showed lesser impacts, the optimal ratio of 1:25 mg/mL effectively reduced mass transfer resistance [19]. According to the results of each test factor, the optimal combination of process conditions for grape seed protein extraction was A₂B₂C₃D₂, i.e., extraction temperature of 45℃, extraction time of 90 min, extraction solution pH of 11.0, and material-liquid ratio of 1:25 mg/mL, which resulted in the highest extraction rate of grape seed protein, achieving a stable yield of 48.68±0.29% in triplicate experiments.

Table 3. Results of the orthogonal test for the extraction of grape seed protein.

	Temperature(℃) A	Time(min) B	pH C	Material-liquid ratio（w/v） D	Extraction yield（%）
1	1	1	1	1	41.39±0.46
2	1	2	2	2	40.64±0.87
3	1	3	3	3	45.51±0.24
4	2	1	2	3	44.90±1.03
5	2	2	3	2	48.13±0.77
6	2	3	1	1	42.89±0.52
7	3	1	3	2	47.92±0.06
8	3	2	1	3	46.77±0.79
9	3	3	2	1	43.60±0.81
K1	127.54	134.21	131.05	127.88
K2	135.92	135.54	129.14	136.69
K3	138.29	132.00	141.56	137.18
R	10.75	3.54	12.42	9.3
Sequence	C>A>D>B
Optimal level	A2	B2	C3	D2
Optimal combination	A2B2C3D2

3.3 Amino acid composition of grape seed protein

Amino acid composition analysis showed that the total content of hydrophobic amino acids such as Gly, Ala, Val, Met, Ile, Leu, Phe and Pro in grape seed protein was 33.56% (Table 4). The non-polar side chains of hydrophobic amino acids have the property of reducing surface tension, which facilitates the formation of a hydrophobic core in the three-dimensional structure of the protein [20]. This structural feature enables them to react with free radicals by providing hydrogen atoms or electrons, thus effectively terminating free radical chain reactions. Meanwhile, the interaction of its hydrophobic side chain with the cell membrane phospholipid bilayer also enhances membrane stability and reduces inflammatory factor-mediated oxidative damage [21]. Combined with the amino acid composition data determined in this study, grape seed protein peptides may exert antioxidant and anti-inflammatory effects through the multiple mechanisms described above, a finding that provides an important theoretical basis for subsequent functional activity studies.

Table 4. Amino acid composition of grape seed protein.

Amino acid	Content of amino acids（%）
Asp	8.16±1.4
Thr	2.24±0.2
Ser	3.60±1.2
Glu	21.06±1.5
Gly	5.21±0.2
Ala	4.10±0.8
Val	5.45±2.4
Met	1.05±0.7
Ile	4.03±1.3
Leu	6.05±1.0
Tyr	3.63±0.2
Phe	3.97±1.9
Lys	2.26±1.6
His	1.85±1.6
Arg	7.48±1.5
Pro	3.70±1.9

3.4 Process optimization for the preparation of GSPP

Determination of free radical scavenging rate of grape seed protein digests of five proteases showed that digests of alkaline protease presented the strongest radicals scavenging acticity of ABTS+· and DPPH·, so alkaline protease will be selected for GSPP preparation in subsequent experiments.

(A)	(B)

Figure 3. ABTS radical scavenging activity (A) and DPPH radical scavenging activity (B) of protease hydrolysates.

A three-factor, three-level response surface test was used to further optimize the effects of three enzymatic conditions, namely enzyme addition, enzymatic temperature and enzymatic time, on the free radical scavenging rate of GSPP. The results of ABTS+· radical scavenging and DPPH radical scavenging of GSPP under each condition are shown in Table 5.

Table 5. Box-Benhnken experiment design and results.

	Enzyme concentration(U/mg) A	Temperature(℃) B	Time(min) C	ABTS radical scavenging activity /%	DPPH radical scavenging activity /%
1	0	0	0	76.17	73.72
2	0	1	-1	71.82	69.59
3	-1	-1	0	70.88	74.81
4	1	-1	0	77.57	67.29
5	1	1	0	76.74	78.96
6	0	0	0	75.21	73.58
7	1	0	-1	76.87	66.01
8	0	0	0	74.33	75.4
9	1	0	1	75.43	72.34
10	-1	0	1	70.01	59.34
11	0	-1	-1	68.87	60.37
12	0	0	0	76.72	74.85
13	0	1	1	71.83	62.13
14	0	0	0	77.43	77.58
15	0	-1	1	75.43	75.00
16	-1	1	0	69.75	61.85
17	-1	0	-1	72.99	71.80

Employing Design-Expert 13.0 software, the free radical scavenging activities of the digests from each group in the response surface test were analyzed. Regression equations were generated, correlating enzyme concentration (A), temperature (B), and time (C) with the ABTS radical scavenging activity (Y₁) and DPPH radical scavenging activity (Y₂) of GSPP:

Y₁=75.97+2.87A-0.3262B+0.2688C+0.075AB+0.385AC-1.64BC-0.1998A²-2.04B²-1.95C²

Y₂=75.03+2.1A-0.6175B+0.13C+6.16AB+4.7AC-5.52BC-1.85A²-2.45B²-5.8C²

The ANOVA of the regression models for ABTS and DPPH radical scavenging activities are shown in Table 6 and Table 7. In both models, F-value > 1 and p-value < 0.05 proved that the models were significant overall. The out-of-fit term with p-value > 0.05 can be considered that the model is a good fit for the experimental results and can predict the ABTS and DPPH radical scavenging activity of GSPP more accurately.

Table 6. ANOVA of regression equation for ABTS radical scavenging activity.

Source	Sum of Squares	df	Mean Square	F-value	p-value	Significance
Model	114.83	9	12.76	3.77	0.0471	*
A	66.01	1	66.01	19.49	0.0031	**
B	0.8515	1	0.8515	0.2514	0.6315
C	0.5778	1	0.5778	0.1706	0.692
AB	0.0225	1	0.0225	0.0066	0.9373
AC	0.5929	1	0.5929	0.175	0.6882
BC	10.73	1	10.73	3.17	0.1184
A²	0.168	1	0.168	0.0496	0.8301
B²	17.48	1	17.48	5.16	0.0574
C²	15.97	1	15.97	4.71	0.0665
Rrsidual	23.71	7	3.39
Lack of Fit	17.71	3	5.9	3.93	0.1094	ns
Pure Error	6	4	1.5
Cor Total	138.54	16

Table 7. ANOVA of regression equation for DPPH radical scavenging activity.

Source	Sum of Squares	df	Mean Square	F-value	p-value	Significance
Model	596.83	9	66.31	13.65	0.0012	**
A	35.28	1	35.28	7.26	0.0309	*
B	3.05	1	3.05	0.6279	0.4541
C	0.1352	1	0.1352	0.0278	0.8722
AB	151.66	1	151.66	31.22	0.0008	***
AC	88.27	1	88.27	18.17	0.0037	**
BC	121.99	1	121.99	25.11	0.0015	**
A²	14.4	1	14.4	2.96	0.1288
B²	25.26	1	25.26	5.2	0.0566
C²	141.85	1	141.85	29.2	0.001	***
Rrsidual	34.01	7	4.86
Lack of Fit	23.52	3	7.84	2.99	0.1589	ns
Pure Error	10.49	4	2.62
Cor Total	630.84	16

Among the three tested factors, the amount of enzyme concentration had a significant effect (p < 0.01) on the ABTS and DPPH radicals scavenging activity of GSPP. From the two-dimensional projection contour plots and their three-dimensional response surfaces, it can be seen that the three interactions of enzyme concentration, temperature and time (AB, AC and BC) all had significant effects on the DPPH radical scavenging activity of the enzyme digests, with the strongest interaction effect being that of AB (enzyme concentration × temperature). In the subsequent process optimization and or actual production, it is necessary to focus on coordinating the matching relationship between temperature and enzyme concentration amount, while avoiding the risk of enzyme inactivation caused by high temperature and long time.

Figure 4. The effect of the interaction of experimental factors on the DPPH radical scavenging rate of GSPP.

The conditions of alkaline protease enzyme digestion of grape seed protein for the preparation of peptides were predicted using Design-Expert 13.0 software, and the optimal combination of enzyme digestion factors obtained were: enzyme addition of 189.82 U/mg, enzyme digestion temperature of 52.47°C, enzyme digestion time of 104.17 min, and enzyme solution pH of 9.5, and the predicted values of ABTS radical scavenging activity of GSPP under this condition were 78.53% and the predicted DPPH radical scavenging rate was 86.95%. In order to better adapt to the actual operation of industrial production, the process conditions were changed to 190 U/mg of enzyme addition, enzyme digestion temperature of 52℃, enzyme digestion time of 105 min, and enzyme solution pH 9.5. According to the experimental verification of the adjusted enzyme digestion conditions, the ABTS radical scavenging activity was 80.90±2.50% and DPPH radical scavenging activity was 88.11±1.33 %, which were close to the predicted values, proving that the optimized process parameters were highly feasible.

3.5 The secondary structure of GSPP

Circular dichroism is a spectroscopic technique used to study the structure and conformation of molecules, and the secondary structure information of protein peptides is obtained by detecting the difference in the absorption of circularly polarized light, and the secondary structure of GSPP is fitted and calculated using the CDNN software, which includes four kinds of structures, including α-helix, β-sheet, β-turn, and random coil, in which the α-helix and β-sheet reflect the order of the protein peptide molecule, and the β-turn and random coil reflect the degree of looseness of the peptide molecules [22]. From the experimental results (Table 8), it can be seen that in GSPPs, parallel type β-sheet reaches a proportion of 50.29%, which is the main secondary structural form of GSPPs, while irregular curling accounts for 27.87%, β-turn accounts for 15.90%, and helical structure only accounts for 5.84%. Therefore, the secondary structure dominated by parallel β-sheet may have an important effect on the antioxidant and anti-inflammatory activities of GSPP.

Table 8. Secondary structure composition of GSPP.

Sample	α-helix	β-sheet	β-turn	random coil
GSPP	5.84%	50.29%	15.90%	27.87%

In terms of antioxidant properties, the parallel β-sheet structure can provide a larger surface area and specific binding sites for GSPP, which facilitates contact and binding with free radicals, leading to more effective scavenging of free radicals [23]. In terms of anti-inflammatory activity, β-sheet facilitates the binding of GSPP to receptors on the surface of inflammatory cells, while a lower content of α-helices can expose more hydrophobic groups [24]. These have an enhancing effect on the antioxidant and anti-inflammatory activities of GSPP.

3.6 The antioxidant activity of GSPP

3.6.1 The free radical scavenging ability of GSPP

Free radicals are closely related to the occurrence and development of oxidative stress and inflammation, the ability to scavenge free radicals is an important indicator of the antioxidant activity. The scavenging effects of different concentrations of GSPP on ABTS cation radical (ABTS+·) and DPPH free radical (DPPH·) were determined using vitamin C (VC) as a positive control. The free radical scavenging rates of both GSPP and VC increased with increasing concentration and showed a dose-dependent pattern. When the concentration of GSPP was 2.0 mg/mL, its ABTS radical scavenging rate was 97.10 ± 0.17%, and its DPPH radical scavenging rate was 97.26 ± 0.40%, which were not significantly different from the positive control VC (p > 0.05).

(A)

(B)

Figure 5. ABTS (A) and DPPH (B) radical scavenging ability of GSPP at different concentrations.

Nonlinear regression fitting was performed using GraphPad Prism 9.5 software to determine the IC₅₀ of GSPP, which were 0.34 mg/mL for ABTS radical scavenging activity and 0.45 mg/mL for DPPH· radical scavenging activity. The free radical scavenging ability of GSPP was significantly stronger than that of grape seed peptide (IC₅₀=5.08 mg/mL) prepared by Yang et al. from Jumbo grapes using alkaline protease and flavor protease in a complex enzymatic digestion [25].

3.6.2 Protective effect of GSPP on oxidative damage of RAW 264.7 cells

3.6.2.1 Establishment of RAW 264.7 Cell Oxidative Damage Model

As shown in Figure 6, GSPP does not exert toxic effects on cells in the concentration range of 3.125 μg/mL-25 μg/mL.ROS is a key indicator of oxidative stress status [26]. When BPS induced oxidative stress in RAW 264.7 cells, ROS level increased significantly, and this change could be quantified by fluorescent probe assay. Under the stimulating effect of BPS, ROS in macrophages were elevated to 15.6 times of normal cells, proving that the oxidative stress modeling was successful. However, GSPP can significantly reduce the intensity of ROS in cells, which indicates that GSPP has a better inhibitory effect on the elevation of ROS induced by BPS and can play a role in protecting cells from oxidative damage (Figure 7).

Figure 6. Effect of GSPP on the survival rate of raw 264.7 cells induced by BPS.

Figure 7. Effect of GSPP on ROS level induced by BPS in RAW 264.7 cells.

3.6.2.2 Effect of GSPP on the levels of T-AOC in RAW 264.7 cells

It has been shown that T-AOC not only reflects static antioxidant reserves, but also reveals the regenerative cycling mechanism and emergency response potential among antioxidant components [27]. In this experiment, after stimulation of mouse macrophages with 10^-7 mol/L BPS, the level of T-AOC in the cells was reduced from 1.42 ± 0.14 mmol/gprot to 0.62 ± 0.10 mmol/gprot (p < 0.01), and the level of total antioxidant capacity was decreased by 56.34%. The levels of T-AOC in macrophages gradually increased under the effect of GSPP, indicating that GSPP can protect cells by increasing T-AOC in oxidatively stressed cells (Figure 8).

Figure 8. Effect of GSPP on the level of T-AOC in RAW 264.7 cells.

3.6.2.3 Effect of GSPP on the content of MDA in RAW 264.7 cells

MDA, as a hallmark product of lipid peroxidation in cell membranes, can be measured to evaluate the extent of damage to cell membranes under stimulation and the results of cytotoxic effects [28]. As shown in Figure 9, BPS significantly increased the content of intracellular MDA in RAW 264.7 cells (p < 0.0001), and different concentrations of GSPP significantly reduced the content of MDA generated as a result of BPS induction (p < 0.05). Under the treatment of 25 μg/mL of GSPP, the MDA content in macrophages was 1.32 ± 0.10 nmol/mgprot, which was significantly reduced compared with the oxidative damage model group. The above conclusions indicate that GSPP can protect oxidatively stressed cells by reducing intracellular MDA content and attenuate cellular damage.

Figure 9. Effect of GSPP on MDA in RAW 264.7 cells.

3.6.2.4 Effect of GSPP on the content of GSH in RAW 264.7 cells

As a core intracellular antioxidant molecule, the production and intracellular accumulation of glutathione (GSH) can directly reflect the redox status and antioxidant reserve capacity of cells [29, 30]. As shown in Figure 10, the GSH content in normal cells was 49.97 ± 2.71 μmol/L, and the GSH content in cells was significantly reduced (p < 0.0001) to only 17.07 ± 1.19 μmol/L under the stimulation effect of 10-7 mol/L BPS, suggesting that BPS disrupts the redox equilibrium state in macrophages (p < 0.0001). Before BPS stimulated the cells to produce oxidative stress, the addition of different concentrations of GSPP for preventive protection of the cells could effectively elevate the content of GSH. under the condition of GSPP treatment concentration of 25 μg/mL, the content of GSH in the mouse macrophage cells was elevated to 30.16 ± 1.66 μmol/L, and this content level had been restored to 60.35% of the normal cell level, suggesting that the GSPP was able to alleviate cellular oxidative damage.

Figure 10. Effect of GSPP on the secretion and production of GSH in RAW 264.7 cells.

3.7 Screening of anti-inflammatory peptides from grapeseed and their effect on inflammatory levels in RAW 264.7 macrophages

3.7.1 Analytical identification and activity prediction of peptides

In GSPP, a total of 9034 peptides were identified, of which 94.00% were protein peptides with molecular weights lower than 1 KDa. According to the biological activity > 0.9, 26 peptides were screened, and then the obtained peptides were predicted for antioxidant (free radical scavenging ability), anti-inflammatory activity and toxicity, and the obtained peptide activity information is shown in Table 9.

Table 9. Prediction of the bioactivity and toxicity of peptide fragments.

Peptide	Free radical scavenging ability score	Predicted anti-inflammatory activity	Predicted toxicity
WGF	0.51076031	0.37234261631965637	Non-Toxin
MEGPFF	0.50877070	0.35952755808830260	Non-Toxin
SHFGF	0.50491881	0.41643151640892030	Non-Toxin
LMYF	0.50434530	0.39657938480377200	Non-Toxin
EGPFF	0.49550295	0.41917276382446290	Non-Toxin
GYFLL	0.49434248	0.41763865947723390	Non-Toxin
FPRP	0.48557964	0.37913981080055237	Non-Toxin
WAPR	0.48389062	0.40530037879943850	Non-Toxin
SFYRAF	0.47233152	0.49008610844612120	Non-Toxin
WLF	0.47054040	0.38981229066848755	Non-Toxin
AIPW	0.46700588	0.33832791447639465	Non-Toxin
LPGF	0.46425664	0.38268688321113586	Non-Toxin
LFW	0.46272990	0.34540459513664246	Non-Toxin
TGPF	0.45755139	0.39792162179946900	Non-Toxin
EFFGF	0.45466346	0.41729262471199036	Non-Toxin
GPFFN	0.44918746	0.43925762176513670	Non-Toxin
HFAFL	0.42615053	0.45958644151687620	Non-Toxin
PGRF	0.42296064	0.39985272288322450	Non-Toxin
FLFA	0.39686006	0.33728626370429990	Non-Toxin
FSGFD	0.38610449	0.43985942006111145	Non-Toxin
FAAF	0.38450539	0.40374425053596497	Non-Toxin
FVF	0.37846497	0.33010777831077576	Non-Toxin
IFSGF	0.37818596	0.37838718295097350	Non-Toxin
QSFF	0.37136716	0.43370476365089417	Non-Toxin
FQSF	0.36586103	0.39314097166061400	Non-Toxin
FDSF	0.34832767	0.40428695082664490	Non-Toxin

3.7.2 Network Pharmacological Analysis

After screening the GenCards and OMIM databases with the keyword “inflammation” and filtering targets with relevance scores ≥ 4, a total of 1092 disease-related targets were identified. SwissTargetPrediction and Comparative Toxicogenomics Database provided 256 compound (peptide)-associated targets. Venny 2.1.0 was used to map overlapping genes, yielding 77 candidate targets for anti-inflammatory peptides (Figure 11).

Figure 11.Venn diagram of peptide fragments and inflammatory disease targets. (“Compond” represents grape seed protein peptide fragments)

The STRING database and Cytoscape were then employed to construct a protein-protein interaction (PPI) network, which contained 77 nodes and 796 edges. High node degrees [31] and central network positions (e.g., TNF in Figure 12) indicated key regulatory roles of these targets in inflammation.

Figure 12. PPI network diagram of intersecting targets.

A GO functional enrichment analysis was conducted on the 77 intersection targets of grape seed peptites and inflammation. The analysis yielded 1723 entries related to biological processes (BP), 70 entries related to cellular components (CC), and 138 entries related to molecular functions (MF). The top 10 entries closely associated with oxidative stress in each category (BP, CC, and MF) were selected for visualization (Figure 13).The analysis indicates that these peptides may modulate inflammatory responses, cell signaling pathways, and protein metabolism. Potential targets are primarily localized within cell membranes, vesicles, and protein complexes. These peptides exert their anti-inflammatory effects by regulating biological processes, including inflammatory responses, and molecular functions, such as cytokine binding, receptor-ligand interactions, and signaling receptor activation.

Figure 13. Enrichment analysis of three types of gene ontology.

KEGG pathway enrichment analysis revealed significant modulation of key inflammatory signaling pathways (Figure 14), including TNF signaling pathway and IL-17 signaling pathway (p < 0.05). The TNF signaling cascade initiates through receptor activation, which subsequently triggers phosphorylation and degradation of IκBα via the IκB kinase (IKK) complex. This degradation liberates NF-κB dimers, enabling their nuclear translocation and subsequent activation of pro-inflammatory gene transcription [32]. Notably, NF-κB activation not only enhances expression of TNF-α itself but also upregulates multiple cytokine genes, establishing a self-amplifying positive feedback loop [33]. The synergistic interaction between TNF-α/NF-κB and IL-17 signaling pathways appears to play a pivotal role in regulating both the magnitude and persistence of inflammatory responses, which may have particular relevance in nutrition-mediated immune modulation. Combined with the results of the present study, it can be concluded that the peptide’s modulation of signaling pathways such as TNF may be one of the mechanisms behind their anti-inflammatory activity.

Figure 14. Bubble graph of KEGG signaling pathway analysis.

As shown in Figure 15, there are several related genes in the TNF signaling pathway that are closely related to the inflammatory response.. TNF-α initiates signaling by binding to TNF receptors, leading to activation of the IKK complex through TNF receptor-associated death domain proteins [34]. This activation triggers phosphorylation and subsequent ubiquitin-mediated degradation of IκB proteins, releasing the NF-κB p65/p50 heterodimer [35]. The free dimer translocates to the nucleus where p65 directly binds to promoter regions of target genes, including pro-inflammatory cytokines (IL-6, IL-8), adhesion molecules (ICAM-1), and immune/apoptosis-related genes, thereby regulating key biological processes [36]. The 1MY5 crystal structure of p65’s dimerization domain (RELA) provides a valuable tool for studying NF-κB/IκBα complex formation and pathway modulation, particularly relevant for investigating food-derived bioactives’ regulatory effects on inflammatory responses.

Figure 15. The map of the TNF signaling pathway illustrating the enrichment of 18 target genes (red color).

3.7.3 Molecular Docking of Peptitess and Key Targets

Molecular docking can calculate the binding affinity of the compound to the target by the binding free energy, when the value of the binding free energy is negative it means that the system is more stable after the binding of the receptor to the ligand under isothermal and isobaric conditions. When the binding energy is less than -1.2 kcal/mol, the docking results can be considered reliable, and the larger the absolute value the higher the binding possibility and the better the simulated binding effect. Among the 26 peptides screened for docking with RELA (NF-κB p65) protein, the binding energies of all the docked peptides were less than -5 kcal/mol, which proved that these peptides could spontaneously bind to RELA and the system was stable[37], providing strong support for further research on this signaling pathway. Previous studies have shown that  bioactives can act on Tnf, IKKβ, Mmmp3, cIAP1/2, and other targets to regulate TNF signaling pathway, thereby reducing inflammation [38]. Based on the results of the above experiments, eight peptides with potentially better activity were screened, namely WGF, SHFGF, EGPFF, WAPR, SFYRAF, HFAFL, PGRF, and FDSF, and their molecular docking results are shown in Figure 16. further made into synthetic peptides (with purity of more than 95%) and used as the subjects of subsequent inflammation-related experiments.

RELA-WGF（-5.8 kcal/mol） (A)	RELA- SHFGF（-7.0 kcal/mol） (B)
RELA- EGPFF（-6.8 kcal/mol） (C)	RELA- WAPR（-6.8 kcal/mol） (D)
RELA- SFYRAF（-6.0 kcal/mol） (E)	RELA- HFAFL（-5.5 kcal/mol） (F)
RELA- PGRF（-7.0 kcal/mol） (G)	RELA- FDSF（-7.3 kcal/mol） (H)

Figure 16. Molecular docking results of peptides and RELA (p65).

3.7.4 Peptides from grape seed inhibit the release of cytokines TNF-α, IL-6 and NO in BPS-stimulated RAW264.7 cells

First, the cytotoxicity of peptites was evaluated using the MTT assay. Based on the experimental results, a synthetic peptide concentration of 10.0 μg/mL was used in subsequent experiments to ensure that there was no significant toxic effect on macrophages. In this study (Fig. 1), compared with the control group, the levels of TNF-α, IL-6, and NO in RAW 264.7 cells in the BPS group (model group) were significantly increased (p < 0.01). Peptites treated can reduce the levels of these inflammatory factors compared with the model group (p < 0.05).

(A)

(B)

(C)

(D)

Figure 17. Effects of 8 synthetic peptides on RAW 264.7 cells viability (A) and their effects on TNF-α (B), IL-6 (C), NO (D).

3.7.5 Peptides from grape seed suppressed the mRNA expression of p65、Tnfα、Il6、Il1b and Nos2 in BPS-stimulated RAW 264.7 cells

To further validate the anti-inflammatory activity of grape seed peptides, the mRNA expression levels of inflammation-related genes were verified using RT-qPCR. As a key subunit of the NF-κB complex, p65 enters the nucleus and initiates the transcription of pro-inflammatory genes (Tnfα, Il6, Nos2) during inflammation [39]. Meanwhile, under the stimulation of inflammation, inflammatory signals promote the transcription of the Tnfα gene [40, 41], and TNF-α can bind to its receptor through autocrine or paracrine secretion, further activating the NF-κB and MAPK pathways and forming a positive feedback loop to amplify the inflammatory effects [42]. In addition, the expression of Il6 [43] and Nos2 [44, 45] mRNAs was similarly regulated by the NF-κB signaling pathway, and the expression of Il1b mRNA was mainly regulated through the activation of the NLRP3 inflammasome [46, 47]. Compared with the control group, the expression of p65, Tnfα, Il6, Il1b, and Nos2 mRNA in the cells of the model group was significantly increased (p < 0.01), indicating that BPS has a significant effect on the inflammatory triggering of macrophages, and all the 8 synthetic peptides could inhibit the expression of the mRNA of these inflammation-related genes, realizing the regulation on cellular inflammation (p < 0.05).

(A)	(B)
(C)	(D)
(E)

Figure 18. Effect of 8 synthetic peptides on the mRNA expression of inflammatory cytokines induced by BPS in RAW 264.7 cells.

4. Conclusion

In conclusion, in the present study, 96% of the phenolics in grape seeds were removed by weak base extraction, and the process conditions of protein extraction and protein peptide preparation were optimized to prepare GSPP with good free radical scavenging ability, with IC₅₀ values ABTS+· and DPPH were calculated as 0.34 mg/mL and 0.45 mg/mL. BPS, a potentially risky substance in food, was used as an inducer to establish the RAW 264.7 oxidative stress and inflammation cell model, the antioxidant activity of GSPP was evaluated by measuring the reduction of ROS and MDA levels, and the elevation of T-AOC and GSH content in cells. Among the 9034 peptides identified, eight peptides with anti-inflammatory potential, namely WGF, SHFGF, EGPFF, WAPR, SFYRAF, HFAFL, PGRF and FDSF, were screened for synthesis and their anti-inflammatory activities were verified in cellular models by database prediction, network pharmacology and molecular docking. The results showed that the synthesized peptides could effectively reduce the production and accumulation of NO in the inflammatory cells, decrease the content of cellular inflammatory factors TNF-α and IL-6, and inhibit the expression of p65, Tnfα, Il6, Il1b, and Nos2 mRNA expression, which in turn exerted anti-inflammatory effects. In summary, based on the results of this study and previous studies on active peptides, peptides from wine grape seeds have a greater potential as important resource by-products in antioxidant and anti-inflammatory applications. In the future, research can be conducted on the development of antioxidant and anti-inflammatory products for grape seed peptides.

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