Preparation and Identification of Peptides with α-Glucosidase Inhibitory Activity from Shiitake Mushroom (Lentinus edodes) Protein

The shiitake mushroom is the most commonly cultivated edible mushroom in the world, and is rich in protein. This study aims to obtain the peptides with α-glucosidase inhibition activity from shiitake mushroom protein hydrolysate. The conditions of enzymatic hydrolysis of shiitake mushroom protein were optimized by response surface test. The results showed that the optimal conditions were as follows: the E/S was 3390 U/g, the solid–liquid ratio was 1:20, the hydrolysis temperature and time were 46 °C and 3.4 h, respectively, and the pH was 7. The active peptides were separated by gel filtration and identified by LC-MS/MS analysis and virtual screening. The results indicated that fourteen peptides were identified by LC-MS/MS. Among them, four new peptides (EGEPKLP, KDDLRSP, TPELKL, and LDYGKL) with the higher docking score were selected and chemically synthesized to verify their inhibition activity. The IC50 values of EGEPKLP, KDDLRSP, TPELKL, and LDYGKL for α-glucosidase inhibition activity ranged from 452 ± 36 μmol/L to 696 ± 39 μmol/L. The molecular docking results showed that the hydrogen bond and arene–cation bond were the two major interactions between four peptides and 2QMJ. The hydrogen bonds were crucial to the inhibition activity of α-glucosidase. The results indicate the potential of using the peptides from shiitake mushroom protein as functional food with α-glucosidase inhibition activity.


Introduction
Diabetes mellitus (DM) is a widespread metabolic disorder that affected 537 million adults globally in 2021 [1]. The number of diabetics is predicted to rise to 643 million by 2030 [2]. Two varieties of DM exist: type I (T1DM) and type II (T2DM). It is estimated that the number of people suffering from T2DM accounts for 90% of the total number of diabetics [3], and T2DM is characterized by hyperglycemia. Chronic damage and dysfunction of various tissues are caused by long-term hyperglycemia, such as cardiovascular disease, retinal damage, chronic kidney disease, and diabetic ketoacidosis [4][5][6].
The most effective treatment for T2DM is to reduce hyperglycemia, especially postprandial hyperglycemia, by slowing down the carbohydrate metabolism. The membrane-bound α-glucosidase, located in the small intestine's epithelial mucosa, is a major digestive enzyme involved in carbohydrate metabolism. It can cleave the glycoside bonds in carbohydrate to free the glucose [7,8]. Therefore, inhibition of α-glucosidase activity has evolved to be an effective method for T2DM treatment because of its role in delaying the hydrolysis of solution pH was adjusted to 9.0 with 1 mol/L NaOH. The mixture was sonicated at 40 KHz and 50 • C for 60 min, then centrifugation was used to separate the supernatant for 15 min at a speed of 4000 rpm. The supernatant pH was adjusted to 3.5 by 1 mol/L HCl. Then, after centrifugation at 5000 rpm/min for 20 min, the precipitates were collected.
Hydrolysate preparation: Two grams of protein powder was dissolved in deionized water according to Section 2.2.2. A certain amount of protease was added according to Section 2.2.2, and mixed once the pH value was adjusted to an ideal level according to Section 2.2.2. The mixture was shaken at 150 rpm/min for a certain period time according to Section 2.2.2. The reaction was terminated in boiling water for 5 min. The pH was adjusted to 7.0 with 1 mol/L NaOH or 1 mol/L HCl according to the hydrolysis pH. Then the supernatant was collected after centrifugation at 5000 rpm/min for 20 min.

Response Surface Methodology
The hydrolysis conditions of shiitake mushroom protein were improved using CCD. The inhibitory activity of α-glucosidase acted as the response value. According to the single factor experiment, E/S (A), temperature (B), and time (C) were selected to be optimized for response surface optimization. Table 1 displays the levels of the experimental factors.

Determination of α-Glucosidase Inhibition Activity
A quantity of 100 µL α-glucosidase solution (0.2 U/mL) was premixed with 50 µL sample solution in the enzyme label plate, and incubated at 25 • C for 10 min; 50 µL of PNPG (5 mmol/L) solution was added to start reactions, and incubation was continued for another 30 min at 37 • C. The reaction was terminated with 50 µL 0.67 mol/L Na 2 CO 3 solution. The absorbance value was measured at 405 nm using an AMR-100 automatic enzyme label analyzer. The results were calculated using the following formula: The hydrolysate solution was separated by ultrafiltration membranes of 1 k Da, 3 k Da, and 5 k Da. The fraction was named UF-I (>5 k Da), UF-II (3-5 k Da), UF-III (1-3 k Da), and UF-IV (<1 k Da), and collected separately.

Gel Column Filtration
The fraction F-IV obtained by ultrafiltration was further separated by gel filtration according to Chen et al.'s method [36]. The sample was loaded onto a Sephadex G-10 gel filtration column (2.6 cm × 100 cm) after being dissolved in deionized water. Following that, deionized water was used to elute the peptides at a flow rate of 0.5 mL/min. The eluted fractions were monitored at 280 nm. The desired peak fractions were collected and lyophilized. The α-glucosidase inhibitory rates of elution peaks were determined.

Identification and Screening of the α-Glucosidase Peptide
The fraction with maximum activity obtained by gel filtration chromatography was analyzed by LC-MS/MS according to the method of Chen et al. [36].
The peptides were diluted in a 20 µL solution of 0.1% formic acid and 5% acetonitrile and separates with an Acclaim PepMap RPLC C18 (75 um i.d. × 150 mm,2 um, 100 Å, nanoViper) linked to a PepMap RSLC C18 in an LC-MS/MS system. The mobile phase was as follows: mobile phase A (0.1% formic acid) and mobile phase B (80% acetonitrile with 0.1% formic acid); the gradient elution was performed with a gradient of 3-99% B (0-34 min) and the flow rate was 500 nL/min. MS data were acquired over the range from 100 to 1550 m/z in ESI positive mode. Resolution was 120,000, AGC target was 4 × 10 5 , and maximum IT was 50 ms. MS/MS scanning conditions were as follows: resolution was 30,000, AGC target was 1 × 10 5 , maximum IT was 100 ms, TopN was 20, and NCE/stepped NCE was 32.
Peptide sequences were identified using PEAKS Studio X software in combination with a shiitake mushroom protein database search.

Molecular Docking Analysis
The semi-flexible molecular docking was performed between the identified peptides and the crystal structure of the human α-glucosidase (PDB code: 2QMJ) using MOE software according to Chen et al.'s method [36]. The crystal structure of 2QMJ (PDB DOI: 10.2210/pdb2QMJ/PDB) was downloaded from the PDB database. The structures of 14 peptides identified by LC-MS-MS were drawn using MOE2009 software.
To obtain the receptor molecules required for docking, the water molecules from 2QMJ were eliminated, the molecules were protonated, and energy was minimized. The receptor pocket of the 2QMJ was used as the docking target for molecular docking, and the peptides were used as the ligand. Each coupling was performed 30 times. The peptide with a better docking effect was screened from the obtained results for further analysis.

Peptide Synthesis
The screened potential α-glucosidase inhibitory peptides were chemically synthesized by China Peptides Co., Ltd. (Shanghai, China). Fmoc solid-phase synthesis was used to obtain the raw peptide. Briefly, chlorine resin was swelled by dichloromethane (DCM). Fmoc-AA (amino acid)-OH, O-benzotriazol-1-yl-tetramethyluronium hexafluorophosphate (HBTU), and N,N-diisopropylethylamine (DIEA) were added to the drained resin for 30 min with a nitrogen bubble reaction to accomplish the condensation reaction. The resin was then washed with DMF to remove Fmoc. After being drained, cutting solution (trifluoroacetic acid (TFA)/H 2 O/1,2-ethanedithiol (EDT)/triisopropylsilane (Tis) = 95/1/2/2, v/v/v/v) was added to the resin to cut the peptide. The synthetic peptides were purified by HPLC. The molecular weight of the purified peptide was confirmed by mass spectrometry.

Statistical Analysis
All experiments had three repetitions. Data are expressed as mean ± standard deviation. Statistical significance (Duncan's test, p < 0.05) was analyzed by DPS. Response surface data were analyzed using Design-ExpertV8.0.6 software.

Protease Selection
The α-glucosidase inhibition activity of shiitake mushroom protein hydrolysates obtained by hydrolysis with the acid protease, alkaline proteinase, neutral proteinase, flavor proteinase, papain, and pepsin was investigated. The results indicate that the enzyme specificity of the different proteases determines the activity characteristics of the hydrolysate. The α-glucosidase inhibition rates of the hydrolysates obtained with acid protease, alkaline proteinase, neutral proteinase, flavor proteinase, papain, and pepsin were 12.5 ± 0.31%, 14.8 ± 0.35%, 61.5 ± 1.6%, 33.2 ± 0.71%, 10.7 ± 0.28%, and 15.3 ± 0.37%, respectively. Compared with other hydrolysates, the neutral proteinase hydrolysate showed the maximum inhibitory rate against α-glucosidase at the same concentration. Therefore, neutral protease was chosen as the enzyme for hydrolysis.

Effects of Different Enzyme Concentrations on the Activity of Hydrolysates
The effect of enzyme concentration on the α-glucosidase inhibitory activity of shiitake mushroom protein hydrolysates was investigated. With the increase in the enzymesubstrate ratio (E/S), the α-glucosidase inhibitory rate increased first and then slightly decreased ( Figure 1A). The α-glucosidase inhibitory rate of hydrolysate with E/S of 3000 U/g was significantly higher than that of 1000 U/g and 2000 U/g (p < 0.05). Therefore, 3000 U/g was selected as the appropriate enzyme concentration.

Protease Selection
The α-glucosidase inhibition activity of shiitake mushroom protein hydrolysates obtained by hydrolysis with the acid protease, alkaline proteinase, neutral proteinase, flavor proteinase, papain, and pepsin was investigated. The results indicate that the enzyme specificity of the different proteases determines the activity characteristics of the hydrolysate. The α-glucosidase inhibition rates of the hydrolysates obtained with acid protease, alkaline proteinase, neutral proteinase, flavor proteinase, papain, and pepsin were 12.5 ± 0.31%, 14.8 ± 0.35%, 61.5 ± 1.6%, 33.2 ± 0.71%, 10.7 ± 0.28%, and 15.3 ± 0.37%, respectively. Compared with other hydrolysates, the neutral proteinase hydrolysate showed the maximum inhibitory rate against α-glucosidase at the same concentration. Therefore, neutral protease was chosen as the enzyme for hydrolysis.

Effects of Different Enzyme Concentrations on the Activity of Hydrolysates
The effect of enzyme concentration on the α-glucosidase inhibitory activity of shiitake mushroom protein hydrolysates was investigated. With the increase in the enzymesubstrate ratio (E/S), the α-glucosidase inhibitory rate increased first and then slightly decreased ( Figure 1A). The α-glucosidase inhibitory rate of hydrolysate with E/S of 3000 U/g was significantly higher than that of 1000 U/g and 2000 U/g (p < 0.05). Therefore, 3000 U/g was selected as the appropriate enzyme concentration.

Effects of Different Temperature on the Activity of Hydrolysates
Temperature is a key influencing factor for enzymatic reaction. Increasing temperature can speed up the reaction rate, but excessive temperature can cause protease inactivation. The effect of different temperatures on the α-glucosidase inhibition activity of enzymatic hydrolysates was investigated. With the increase in temperature, the inhibitory rate of α-glucosidase first increased and then decreased. When the temperature reached 45 °C, the maximum inhibitory rate of α-glucosidase was attained ( Figure 1B). Therefore, 45 °C was chosen as the appropriate condition.

Effects of Different Temperature on the Activity of Hydrolysates
Temperature is a key influencing factor for enzymatic reaction. Increasing temperature can speed up the reaction rate, but excessive temperature can cause protease inactivation. The effect of different temperatures on the α-glucosidase inhibition activity of enzymatic hydrolysates was investigated. With the increase in temperature, the inhibitory rate of α-glucosidase first increased and then decreased. When the temperature reached 45 • C, the maximum inhibitory rate of α-glucosidase was attained ( Figure 1B). Therefore, 45 • C was chosen as the appropriate condition.

Effects of Different Substrate Concentrations on the Activity of Hydrolysates
The substrate concentration is a key parameter of enzymatic reaction. When the solid-liquid ratio was 15, the inhibitory rate of enzymatic hydrolysates was lower. When the solid-liquid ratio was 20, the α-glucosidase inhibitory rate of enzymatic hydrolysates increased and then decreased slightly ( Figure 1C). It may be that when the solid-liquid ratio was 15, the protein concentration was too high, which affected the fluidity of the system and the contact surface between the enzyme and the substrate. When the solid-liquid ratio reached 20, the hydrolysate had good fluidity. Therefore, the solid-liquid ratio of 20 was chosen as the appropriate condition for enzymatic hydrolysis.

Effects of Different Enzymolysis Time on the Activity of Hydrolysates
The inhibition activity against α-glucosidase was significantly influenced by the enzymolysis time. With the increase in enzymolysis time, the inhibitory rate of α-glucosidase first increased and reached the highest level at 3 h, and then decreased ( Figure 1D), which may be because some active peptides were further hydrolyzed with the increase in time. Therefore, 3 h was chosen as the appropriate condition for enzymatic hydrolysis.

Effects of Different pH on the Activity of Hydrolysates
With the increase in pH, α-glucosidase inhibitory rates of hydrolysates rose first and then decreased. When pH was 7, the inhibitory rate of α-glucosidase reached the highest level ( Figure 1E). There were no significant differences in the α-glucosidase inhibitory rates among pH 6.0, pH 7.0, and pH 8.0. Therefore, pH 7 was selected as the appropriate condition.

Response Surface Analysis
In the actual process of enzymolysis, various factors may interact with each other. To further investigate the significance of the influence of various factors on the target value, E/S, temperature, and time were taken as independent variables, and α-glucosidase inhibitory rate as the response value, and a CCD experiment was conducted. The results are shown in Table 2. The quadratic polynomial regression equation of α-glucosidase inhibitory rate and E/S (A), enzymolysis temperature (B), and enzymolysis time (C) was obtained as follows: According to the variance analysis results (Table 3), the regression model has a high level of significance (p < 0.0001). The R 2 was 0.9877, which indicates that the test value and the fitting value had a high correlation. The regression equation model fitted the data well because the p value of the lack of fit was not significant. Consequently, it is possible to forecast the test results using the model. It can be seen from Table 3 and Figure 2 that there were significant interactions between temperature and time, and E/S and time.

Separation of Peptides by Ultrafiltration
The molecular weight of the peptide is closely related to the α-glucosidase inhibitory activity. As shown in Figure 3, four fractions were obtained by ultrafiltration from shiitake mushroom protein hydrolysates. The α-glucosidase inhibitory rate of UF-IV was 65.0 ± 3.1%, which was higher than that of the other three fractions. The results indicate that the α-glucosidase inhibitory activity increased with the decrease in molecular weight, which is consistent with the results of previous reports [19,27,37]. Therefore, fraction UF-IV was  The optimal conditions predicted by the model were as follows: the E/S was 3390 U/g, the solid-liquid ratio was 1:20, the hydrolysis temperature and time were 46 • C and 3.4 h, respectively, and the pH was 7. Under optimal conditions, the predicted value of the α-glucosidase inhibitory rate was 65.2%. To verify the reliability of the predictive value of the model, three validation tests were carried out under optimum conditions, and the average α-glucosidase inhibitory rate of hydrolysate was 64.6 ± 1.2%. The result indicates that the optimized parameters of the model were accurate and reliable.

Separation of Peptides by Ultrafiltration
The molecular weight of the peptide is closely related to the α-glucosidase inhibitory activity. As shown in Figure 3, four fractions were obtained by ultrafiltration from shiitake mushroom protein hydrolysates. The α-glucosidase inhibitory rate of UF-IV was 65.0 ± 3.1%, which was higher than that of the other three fractions. The results indicate that the α-glucosidase inhibitory activity increased with the decrease in molecular weight, which is consistent with the results of previous reports [19,27,37]. Therefore, fraction UF-IV was selected for further purification.

Separation of Peptides by Gel Filtration
Peptides are frequently purified by gel filtration. The UF-IV fraction was furth arated by a gel filtration column (Sephadex G-10). The results are presented in Figu total of eleven peaks were detected and named as F1-F11. The 11 fractions were co to study their α-glucosidase inhibitory activity. Fraction F5 exhibited the strongest cosidase inhibition rate. The inhibition rate of F5 was 61.3 ± 0.69%.

Separation of Peptides by Gel Filtration
Peptides are frequently purified by gel filtration. The UF-IV fraction was further separated by a gel filtration column (Sephadex G-10). The results are presented in Figure 4. A total of eleven peaks were detected and named as F1-F11. The 11 fractions were collected to study their α-glucosidase inhibitory activity. Fraction F5 exhibited the strongest αglucosidase inhibition rate. The inhibition rate of F5 was 61.3 ± 0.69%.

Identification and Screening of α-Glucosidase Inhibitory Peptides
The sequences of fraction F5 were determined by HPLC-MS/MS with de novo sequencing. Fourteen peptides were identified from fraction F5 (score > 80). The molecular weight of the fourteen peptides ranged from 699 Da to 829 Da ( Table 4).
The binding energy and binding sites of peptides to receptor proteins are related to their biological activity. To further screen active peptides from these 14 peptides, the peptides docking with the crystal structure of α-glucosidase (2QMJ) were performed using MOE software. Peptides with binding energy below −14 as well as more than four binding bonds were selected, as shown in Table 5, which were EGEPKLP, KDDLRSP, TPELKL, and LDYGKL ( Figure 5). The α-glucosidase inhibition activity of the four peptides was further investigated. EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed an effective inhibition activity, and their IC50 value was 499 ± 39 µmol/L, 550 ± 37 µmol/L, 452 ± 36 µmol/L, and 696 ± 39 µmol/L, respectively.

Identification and Screening of α-Glucosidase Inhibitory Peptides
The sequences of fraction F5 were determined by HPLC-MS/MS with de novo sequencing. Fourteen peptides were identified from fraction F5 (score > 80). The molecular weight of the fourteen peptides ranged from 699 Da to 829 Da (Table 4).    The binding energy and binding sites of peptides to receptor proteins are related to their biological activity. To further screen active peptides from these 14 peptides, the peptides docking with the crystal structure of α-glucosidase (2QMJ) were performed using MOE software. Peptides with binding energy below −14 as well as more than four binding bonds were selected, as shown in Table 5, which were EGEPKLP, KDDLRSP, TPELKL, and LDYGKL ( Figure 5). The α-glucosidase inhibition activity of the four peptides was further investigated. EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed an effective inhibition activity, and their IC50 value was 499 ± 39 µmol/L, 550 ± 37 µmol/L, 452 ± 36 µmol/L, and 696 ± 39 µmol/L, respectively. The binding energy and binding sites of peptides to receptor proteins are related to their biological activity. To further screen active peptides from these 14 peptides, the peptides docking with the crystal structure of α-glucosidase (2QMJ) were performed using MOE software. Peptides with binding energy below −14 as well as more than four binding bonds were selected, as shown in Table 5, which were EGEPKLP, KDDLRSP, TPELKL, and LDYGKL ( Figure 5). The α-glucosidase inhibition activity of the four peptides was further investigated. EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed an effective inhibition activity, and their IC50 value was 499 ± 39 µmol/L, 550 ± 37 µmol/L, 452 ± 36 µmol/L, and 696 ± 39 µmol/L, respectively. The binding energy and binding sites of peptides to receptor proteins are related to their biological activity. To further screen active peptides from these 14 peptides, the peptides docking with the crystal structure of α-glucosidase (2QMJ) were performed using MOE software. Peptides with binding energy below −14 as well as more than four binding bonds were selected, as shown in Table 5, which were EGEPKLP, KDDLRSP, TPELKL, and LDYGKL ( Figure 5). The α-glucosidase inhibition activity of the four peptides was further investigated. EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed an effective inhibition activity, and their IC50 value was 499 ± 39 µmol/L, 550 ± 37 µmol/L, 452 ± 36 µmol/L, and 696 ± 39 µmol/L, respectively. The binding energy and binding sites of peptides to receptor proteins are related to their biological activity. To further screen active peptides from these 14 peptides, the peptides docking with the crystal structure of α-glucosidase (2QMJ) were performed using MOE software. Peptides with binding energy below −14 as well as more than four binding bonds were selected, as shown in Table 5, which were EGEPKLP, KDDLRSP, TPELKL, and LDYGKL ( Figure 5). The α-glucosidase inhibition activity of the four peptides was further investigated. EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed an effective inhibition activity, and their IC50 value was 499 ± 39 µmol/L, 550 ± 37 µmol/L, 452 ± 36 µmol/L, and 696 ± 39 µmol/L, respectively.

Discussion
DM is a typical metabolic disease with chronic complications [4][5][6]. With the increasing incidence of diabetes, the controls of blood glucose and therapeutic complications are faced with severe challenges. Intervention with alpha-glucosidase inhibitors can slow the conversion rate of ingested carbohydrates to glucose, thereby preventing glucose from entering the systemic circulation. Compared with the regulatory mechanism of promoting insulin secretion (biguanides, sulfonylureas, DPP IV inhibitors, GLP-1 agonists, etc.), this intervention pathway does not involve islet cells and can relieve the pressure of insulin metabolism to a certain extent and improve the function of damaged islet beta cells. The α-glucosidase in the small intestine plays an important role in the digestion of carbohydrate [19]. Blocking the enzyme with α-glucosidase inhibitors in the digestive tract will limit the digestion of carbohydrates and reduce postprandial hyperglycemia [17,23].
Recently, many studies have confirmed that α-glucosidase inhibitory peptides prepared from food protein have excellent α-glucosidase inhibitory potential [2]. With their

Discussion
DM is a typical metabolic disease with chronic complications [4][5][6]. With the increasing incidence of diabetes, the controls of blood glucose and therapeutic complications are faced with severe challenges. Intervention with alpha-glucosidase inhibitors can slow the conversion rate of ingested carbohydrates to glucose, thereby preventing glucose from entering the systemic circulation. Compared with the regulatory mechanism of promoting insulin secretion (biguanides, sulfonylureas, DPP IV inhibitors, GLP-1 agonists, etc.), this intervention pathway does not involve islet cells and can relieve the pressure of insulin metabolism to a certain extent and improve the function of damaged islet beta cells. The α-glucosidase in the small intestine plays an important role in the digestion of carbohydrate [19]. Blocking the enzyme with α-glucosidase inhibitors in the digestive tract will limit the digestion of carbohydrates and reduce postprandial hyperglycemia [17,23].
Recently, many studies have confirmed that α-glucosidase inhibitory peptides prepared from food protein have excellent α-glucosidase inhibitory potential [2]. With their higher bioavailability and lower side effects, the study of α-glucosidase inhibitory peptides is important. Different proteases have unique restriction sites, and thus biological peptides with different amino acid compositions can be produced by different proteases. Ren et al. [38] found that the hydrolysates released by amylopsin, pancrelipase, alcalase, papain, and trypsin from hemp seed protein displayed different α-glucosidase inhibition activity. The hydrolysates prepared by alcalase showed a high α-glucosidase inhibition rate. In this research, the α-glucosidase inhibition rates of the hydrolysates obtained by acid protease, alkaline proteinase, neutral proteinase, flavor proteinase, papain, and pepsin were investigated. The neutral proteinase hydrolysate showed the maximum α-glucosidase inhibition activity. This indicates that the newer short peptides with enhanced α-glucosidase inhibitory activity had been generated in the hydrolysates by neutral protease.
Ultrafiltration and gel chromatography can be used to further isolate and purify active peptides from the hydrolysates. In this study, low molecular weight peptides (MW < 1 k Da) had stronger α-glucosidase inhibition activity than other fractions (>5 k Da, 3-5 k Da, 1-3 k Da), which is consistent with the results of previous reports [27,37]. Wang et al. [27] reported that the hydrolysate from ginkgo biloba seed protein was fractionated into five fractions (<1 k Da, 1-3 k Da, 3-5 k Da, 5-10 k Da, >10 k Da), and the fraction with the MW < 1 k Da showed the highest α-glucosidase inhibition activity. Liu et al. [37] found that the peptide fraction (<1 kDa) of wheat germ had better α-glucosidase inhibition activity than other factions. This may be explained by the fact that the active site on the amino acid residues in the low molecular weight peptides can be exposed to the outside and increase the possibility of interacting with α-glucosidase.
The amino acid composition, molecular weight, and chain length of the peptides influence biological activity of α-glucosidase inhibitory peptides [39][40][41]. The majority of peptides with α-glucosidase inhibition activity have a short sequence of less than 10 residues [39,40,42]. The amino acid sequences of fraction with the highest activity were determined by HPLC-MS/MS, and 14 peptide sequences (score > 80) were obtained. The molecular weight of these peptides ranged from 699 to 829 Da and the sequence lengths were 6 to 7 amino acids residues. According to previous reports, the short peptide is beneficial to reducing the free energy of peptide-enzyme binding, and improves the inhibitory effect [40]. The identified peptides were further docked to the crystal structure of α-glucosidase. The peptides with lower energy scores and more binding sites may be proposed as active peptides in shiitake mushroom protein hydrolysates. Among these 14 peptides, EGEPKLP, KDDLRSP, TPELKL, and LDYGKL showed lower energy scores and outstanding binding ability to α-glucosidase.
The peptides with more hydrophobic amino acids had better inhibitory activity of α-glucosidase [38,45]. In previous reports, hydrophobic amino acids Leu and Pro were commonly found in the sequence of α-glucosidase inhibition peptides, which contributed greatly to the inhibitory activity, such as LR, PFP, PLMLP, KLPGF, RVPSLM, WLRL, SWLRL and LLPLPVLK [4,16,38,45,46]. Basic amino acids (Lys or Arg) in peptides were also important to α-glucosidase inhibition activity, especially Lys or Arg at the N-terminus [40,47]. The amino acid analysis of the four peptides showed that the proportion of Pro and Leu in EGEPKLP, TPELKL, and LDYGKL was 42.9%, 50.0%, and 33.3%, respectively. Although the proportion of proline and leucine is only 28.6% in KDDLRSP, Lys at the N-terminus may contribute to the activity.
Further study of the interactions between the peptide and α-glucosidase showed that EGEPKLP interacted with the 2QMJ through four hydrogen bonds. KDDLRSP and LDYGKL formed five hydrogen bonds with 2QMJ. TPELKL formed five hydrogen bonds and an arene-cation interaction with 2QMJ. The α-glucosidase inhibition activity is mainly dependent on hydrogen bonding, which agrees with the results of previous research [27,48].

Conclusions
In this study, the optimal conditions for preparation of α-glucosidase inhibitory peptide from shiitake mushroom were established. Four new α-glucosidase inhibitory peptides (EGEPKLP, KDDLRSP, TPELKL, and LDYGKL) were identified from shiitake mushroom protein hydrolysates using LC-MS/MS and virtual screening. TPELKL exhibited a higher α-glucosidase inhibitory activity than EGEPKLP, KDDLRSP, and LDYGKL. The molecular docking results demonstrated that the hydrogen bond and arene-cation bond were the two major interactions between the four peptides and 2QMJ. The hydrogen bond was crucial to the α-glucosidase inhibition activity. The results suggest that shiitake mushroom may be a reliable source of α-glucosidase inhibitory peptide and provide a feasible strategy for further utilization of shiitake mushroom protein. To improve the possibility of using these peptides as supplements, future studies are required, including examination of the effects of the peptides on other enzymes linked to starch digestion, the antidiabetic mechanism of these peptides, and their safety and stability in vivo.

Data Availability Statement:
The datasets generated for this study are available on request from the corresponding author.

Conflicts of Interest:
The authors declare no conflict of interest.