Method for identifying animal-derived new food resource sensitizers and evaluating potential sensitization of animal-derived new food resource sensitizers based on proteomics and bioinformatics

Abstract

The invention belongs to the field of allergen identification and evaluation, and particularly relates to a method for identifying animal-derived new food resource sensitizers and evaluating potential sensitization of the animal-derived new food resource sensitizers based on proteomics and bioinformatics. According to the method, the LC-MS / MS technology is used for obtaining proteome data of a target species, UniProt and AllgenOnline databases are used for performing homology comparison with known allergens, and bioinformatics tools such as AllCatPro2.0 and the like are combined for identifying the allergens in the animal-derived new food resources and evaluating the sensitization potential of the animal-derived new food resources. Proteins with sensitization risks can be rapidly screened out in a large scale at low cost through a bioinformatics method, some new sensitizers can be predicted, whether the target proteins are really expressed or not can be sensitively and accurately verified through the mass spectrum technology, and therefore the method has the advantages of being high in sensitivity and high in sensitivity. The method is a comprehensive, efficient and reliable novel food sensitization evaluation method for combination of mass spectrum-based proteomics and bioinformatics technologies.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of allergen identification and evaluation, and particularly relates to a method for identifying and evaluating potential allergens of animal-derived new food resources based on proteomics and bioinformatics. BACKGROUND

[0002] Under the influence of environmental constraints, climate change and other factors, the global food growth pressure is increasing, and the supply and demand contradiction is intensifying. Exploiting new food resources, improving food yield and quality, and improving the structure have become the research focus at home and abroad. At present, there are few animal-derived new food resources developed and utilized, and it is urgent to exploit and utilize other new food resources, enrich food sources, and improve food supply security.

[0003] Finding new food resources from insects, aquatic animals, and livestock and poultry processing by-products is an important way to obtain nutrients such as protein, oil, and sugar. China has rich insect resources and a long history of consumption, but the exploitation of its protein resources is minimal. With the growth of aquatic products and livestock and poultry production, a large amount of internal organs, bones, and blood by-products have increased, and their food research has great potential. Insects are considered the largest untapped resource, and obtaining food from insects has become an international major new trend.

[0004] Exploiting new animal-derived food resources can help improve food yield and quality, improve dietary structure, promote sustainable agricultural development, and enrich animal-derived food resources. However, animal-derived new food resources such as insects, aquatic animals, and livestock and poultry processing by-products contain potential allergens such as proteins and polypeptides, which pose a risk of allergenicity and pose new challenges to food allergen risk management. However, there is currently a lack of universal and stable allergenicity evaluation methods.

[0005] Currently, there is no cure for food allergies. For patients with mild symptoms, antihistamines and corticosteroids can be used to alleviate allergic reactions; for patients with severe symptoms, epinephrine can be used in addition to antihistamines and corticosteroids for prevention and treatment. Despite these emergency measures, strictly avoiding the intake of allergen food is still the fundamental strategy for preventing allergic reactions. Therefore, evaluating the allergenicity of food ingredients and identifying food allergens has become a key strategy for preventing food allergies.

[0006] So far, the methods for evaluating food ingredient allergenicity mainly include bioinformatics prediction, mass spectrometry, in vivo and in vitro simulated digestion, enzyme-linked immunosorbent assay (ELISA), cell models and animal models. Enzyme-linked immunization and cell models are highly dependent on antigen-antibody specific binding, new food allergen risk is not clear, it is difficult to obtain serum of patients with specific food allergy, there is no corresponding specific antibody for detection, and new allergens cannot be identified. Animal models are high in cost, long in cycle and complex in operation, and cannot efficiently identify allergens in a short period. It is generally believed that proteins that can resist gastrointestinal digestion and reach the intestinal mucosa in the form of macromolecules are more likely to be recognized by the immune system and thus trigger allergies; however, part of the protein is easily digested, but the allergen epitope is not destroyed, and still retains the sensitization ability; some proteins can resist digestion, but lack other characteristics required to trigger the immune system; therefore, although the simulated digestion experiment has the advantages of rapidness, low cost, ease of standardization and operation, the anti-digestion characteristics cannot directly determine that the target protein has allergenicity. SUMMARY

[0007] In view of the problems in the prior art, the present application provides a method for identifying allergens of new animal-derived food resources and evaluating potential allergenicity based on proteomics and bioinformatics.

[0008] The technical scheme adopted by the present application to achieve the above-mentioned purposes is as follows: The present application provides an animal-derived new food resource allergen based on proteomics and bioinformatics, and the allergen is a crustacean tropomyosin or arginine kinase. The crustacean tropomyosin is cicada tropomyosin, and its sequence is: MDAIKKKMQAMKLEKDNAMDRALLCEQQARDANLRAEKAEEEARALQKKIQTIENDLDQTQEQLMQVNAKLEEKDKALQNAESEVAALNRRIQLLEEDLERSEERLATATAKLAEASQAADESERARKVLENRSLADEERMDALENQLKEARFLAEEADKKYDEVARKLAMVEADLERAEERAETGESKIVELEEELRVVGNNLKSLEVSEEKANQREEEYKNQIKTLTTRLKEAEARAEFAERSVQKLQKEVDRLEDELVVEKERYRVIGDGLDLAFVELYGY. The arginine kinase is cicada arginine kinase, and the sequence is: MVDAATLEKLEAGFKKLKASNSKSLLKKYLTQEVFDKLKTLKTPTFGSTLLDVIQSGLENHDSGVGIYAPDAEAYTIFADLFDPIIEDYHGGFKKTDKHPAKDWGDVGSLANLDPAGEYIISTRVRCGRSMEGYPFNPCLTEAQYKEMEGKVSSTLSGLSGELKGNFYPLTGMTKEVQQKLIDDHFLFKEGDRFLQAANACRFWPSGRGIYHNENKTFLVWCNEEDHLRLISMQMGGDLGQVYRRLVTAVNDIEKRIPFSHNDRLGFLTFCPTNLGTTVRASVHIKVPKLAANRAKLEEIACKFNLQVRGTRGEHTEAEGGVYDISNKRRMGLTEYQAVKEMNDGIAELIKIEREL.

[0009] The application provides a method for identifying allergens of animal-derived new food resources and evaluating potential allergenicity based on proteomics and bioinformatics, and comprises the following steps: (1) First, extract, separate and identify proteins from the animal-derived new food resources; (2) According to the LC-MS / MS identification, obtain protein information belonging to the target food resources, and obtain protein amino acid sequences; (3) Input the protein amino acid sequences into an online allergen prediction platform for prediction, screen out proteins with higher sensitization risk, and perform homology comparison with known allergens in a database, and at least two platforms are simultaneously predicted to have higher possibility of sensitization, and the proteins are defined as potential allergens in the target food resources; (4) The protein sequences of the predicted potential allergens and the LC-MS / MS identification results are consistent with the corresponding proteins of other arthropod known allergens, and multiple sequence alignment is performed to obtain a phylogenetic tree, identify the homologous regions of the common allergens, find protein sequences with closer genetic relationship, and evaluate the cross-reactivity.

[0010] Preferably, in step (1), when the extracted sample is a polypeptide sample, the desalted polypeptide sample is dissolved in 0.1% formic acid-water solution and separated by a Nano-HPLC liquid phase system UltiMate 3000 RSLCnano (ThermoFisher Scientific); the liquid phase A is 0.1% formic acid-water solution, and the liquid phase B is 0.1% formic acid-acetonitrile solution; the chromatographic column is Trap column, 100 μm × 20 mm (RP-C18, Agilent), and the column is equilibrated with 100% A liquid at a flow rate of 3 μL / min; the sample is loaded by an automatic sampler and combined with the Trap column, and then separated by an Analysis column, 75 μm × 250 mm (RP-C18, New Objective, USA) chromatographic column, at a flow rate of 300 nL / min; After the enzymatic product is separated by capillary high performance liquid chromatography, mass spectrometry is performed by a Q-Exactive plus mass spectrometer (ThermoFisher Scientific); the detection mode is calibrated by a standard calibration liquid before use, the parent ion scanning range is 300-1500 m / z, the mass spectrometry scanning mode is information-dependent acquisition mode (DDA, Data Dependent Acquisition), 20 strongest fragment maps (MS2 scan) are collected after each full scan, the fragmentation mode is high energy collision dissociation (HCD, high energy collision dissociation), NCE energy is 28, dynamic exclusion time is 25 s; the MS1 resolution is 70000 at M / Z 200, the AGC target is set to 3e6, the maximum injection time is 100 ms, the MS2 resolution is set to 17500, the AGC target is set to 1e5, and the maximum injection time is 50 ms; based on the Raw file obtained by mass spectrometry detection, Proteome Discoverer 2.5 is used to search in the database, and then protein identification is performed based on the database search results.

[0011] Preferably, in step (2), the protein amino acid sequence is searched and obtained by downloading in the UniprotKB database with the ID of the protein as the index.

[0012] Preferably, in step (3), the online allergen prediction platform is AllerCatPro2.0; and the database is AllerCatPro2.0, AllergenOnline and Allermatch™.

[0013] Preferably, in step (4), the multiple sequence alignment uses the Clustal Omega tool.

[0014] Preferably, in step (4), the phylogenetic tree is obtained using the BLOSUM62 algorithm.

[0015] Preferably, in step (4), the common allergens include but are not limited to arthropod tropomyosin and arginine kinase.

[0016] The beneficial effects of the present application are: (1) The present application uses LC-MS / MS technology to obtain the proteome data of the target species, uses UniProt and AllergenOnline databases to perform homology comparison with known allergens, and uses bioinformatics tools such as AllerCatPro2.0 to identify allergens in new animal-derived food resources and evaluate the sensitization potential of the new animal-derived food resources, and for the first time, new allergens are found.

[0017] (2) The present application can quickly, low-cost and large-batch screen proteins with sensitization risk through bioinformatics methods, and predict some new allergens. Mass spectrometry can sensitively and accurately confirm whether the target proteins are truly expressed, so that the combination of mass spectrometry-based proteomics and bioinformatics technology is a comprehensive, efficient and reliable new food allergenicity evaluation method. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a molecular weight distribution diagram of cicada pupa protein; Figure 2 is the analysis result of cicada pupa protein GelAnalyzer; Figure 3 is a total ion flow diagram; Figure 4 is the prediction result of cicada pupa protein AllerCatPro2.0; Figure 5 is the amino acid multiple sequence alignment of the potential allergen arginine kinase of cicada pupa protein; Figure 6 is the phylogenetic analysis of the potential allergen arginine kinase of cicada pupa protein; Figure 7 is the amino acid multiple sequence alignment of the potential allergen tropomyosin of cicada pupa protein; Figure 8 is the phylogenetic analysis of the potential allergen tropomyosin of cicada pupa protein. DETAILED DESCRIPTION

[0019] The technical solutions of the present application will be further explained and described below through specific examples.

[0020] The protein extraction solution containing protease inhibitors used in this invention was purchased from Beijing Solarbio Science & Technology Co., Ltd. The insect protein extraction kit (pull-down, co-IP, etc.) consists of the following three components: Component A: Insect protein extract A Component B: Protein stabilizer B Component C: Mixture of protease inhibitors The specific usage method is as follows: Add 2ul of protease inhibitor mixture C and 4ul of protein stabilizer B to every 500ul of cold insect protein extract A.

[0021] Example 1 1. Proteomics analysis 1.1 Protein Extraction Animal-derived new food resources (insects, aquatic animals, and livestock processing by-products) samples were taken at -80℃ (the cicada nymphs used in this embodiment were cicada pupae). After removing inedible parts, the samples were cut into small pieces, placed in a mortar, and ground into powder using liquid nitrogen. 50-100 mg of the powder was placed in 500 μl of protein extraction buffer containing protease inhibitors (purchased from Beijing Solarbio Science & Technology Co., Ltd., insect protein extraction kit (pull-down, co-IP, etc.)). The buffer was placed on ice for 30 minutes, and mixed with a vortex mixer every 5 minutes. Then, the buffer was centrifuged at 4°C and 14000×g for 10 minutes. The supernatant was taken as the total protein solution of the target food resource (total protein sample of cicada pupae).

[0022] 1.2 Protein Isolation and Identification Total protein from cicada pupae was added to 5×SDS-PAGE loading buffer at a ratio of 4:1, boiled for approximately 10 min, and then 30 μg of protein per well was loaded onto a 10% polyacrylamide gel. Broad-range SDS-PAGE standards were used as molecular markers. After loading, the gel was electrophoresed at a constant voltage of 80 V for 30 min at room temperature, then at a constant voltage of 120 V for 70 min. Electrophoresis was stopped when bromophenol blue reached the bottom of the glass plate. After electrophoresis, the gel was rinsed in pure water for several minutes; the pure water was discarded, and an appropriate amount of Coomassie Brilliant Blue ultra-fast staining solution was added, just enough to cover the gel. Staining was performed on a horizontal shaker at room temperature for 10–15 min. After staining, the staining solution was discarded, and the gel was washed with pure water to remove residual staining.

[0023] The types and approximate molecular weight ranges of proteins in the sample were preliminarily understood by Coomassie blue staining. The corresponding gel strip was cut, and after trypsin enzymolysis and C18 column desalting treatment, LC-MS / MS analysis was performed to obtain a Raw file. Proteome Discoverer 2.5 was used to search the corresponding database, and then based on the results of the database search, protein identification was performed.

[0024] 2. Bioinformatics prediction of allergenicity 2.1 Protein sequence acquisition According to the protein information identified by LC-MS / MS belonging to the target food resource, the protein amino acid sequence FASTA format file was searched and downloaded in UniprotKB database with the ID of the protein as the index.

[0025] 2.2 Protein allergenicity prediction The protein amino acid sequence FASTA format file was input into the online allergen prediction platform AllerCatPro2.0 for prediction, and proteins with higher allergenic risk were screened out and homology comparison was performed with known allergens in Allermatch and AllergenOnline (http: / / www.allergenonline.org / ) databases. Proteins with higher possibility of allergenicity predicted by at least two platforms at the same time were defined as potential allergens in the target food resource.

[0026] 2.3 Cross-reactivity analysis The protein sequences of the predicted potential allergens consistent with the LC-MS / MS identification results were searched in Uniproit database for corresponding proteins of other arthropod known allergens included in WHO / IUIS, and FASTA format protein sequences were downloaded. Clustal Omega (https: / / www.ebi.ac.uk / jdispatcher / msa / clustalo) tool was used for multiple sequence alignment (MSA), and Jalview was used for editing. BLOSUM62 algorithm was used to obtain a phylogenetic tree, which was then visualized in iTOL v6 (https: / / itol.embl.de / ) to identify homologous regions with common allergens (such as crustacean tropomyosin and arginine kinase), find protein sequences with close genetic relationship, and evaluate cross-reactivity.

[0027] Example 2 1. SDS-PAGE From the electrophoresis results, it can be seen that the composition of cicada pupa protein is complex, and there are band distributions in the range of molecular weight 25-100 KDa. Three protein bands with high staining intensity can be observed near the molecular weight of 80, 70, and 55 KDa; as shown in Figure 1 The SDS-PAGE results were analyzed by using software GelAnalyzer 23.1.1. The software automatically recognized the lanes and bands on the gel, and through manual adjustment and correction, the optical density of each band was measured for quantitative analysis, and the relative percentage content of the band was calculated. The standard curve was drawn by using the known molecular weight standard (Marker), and the molecular weight of the total protein sample of the cicada pupa was estimated. Three main protein bands were detected, with molecular weights of about 82, 72, and 54 KDa, accounting for about 22%, 11%, and 9% of the total stained protein amount of each lane, respectively, which was roughly the same as the result observed by the naked eye; as shown in Figure 2 A 72 kDa protein band accounted for about 11% of the total stained protein amount of each lane. In addition, a 54 kDa protein band was also detected, accounting for about 9% of the total stained protein amount of each lane; as shown in Figure 1 、 Figure 2 .

[0028] From Figure 2 , it can be seen that the SDS-PAGE results were analyzed by using software GelAnalyzer 23.1.1. The software automatically recognized the lanes and bands on the gel, and through manual adjustment and correction, the optical density of each band was measured for quantitative analysis, and the relative percentage content of the band was calculated. The standard curve was drawn by using the known molecular weight standard (Marker), and the molecular weight of the total protein sample of the cicada pupa was estimated. Three main protein bands were detected, with molecular weights of about 82, 72, and 54 KDa, accounting for about 22%, 11%, and 9% of the total stained protein amount of each lane, respectively.

[0029] 2. LC-MS / MS identification results According to the electrophoresis results, all gel strips with molecular weights in the range of 25-100 KDa were cut and subjected to LC-MS / MS analysis. The analytical instrument used in the experiment was an Ultimate 3000 nano ultra-high performance liquid chromatography tandem Q Exactive plus high-resolution mass spectrometer system; the specific process was as follows: The desalted polypeptide sample was re-dissolved in 0.1% formic acid-water solution and separated using a Nano-HPLC liquid phase system UltiMate 3000 RSLCnano (ThermoFisher Scientific). The liquid phase A was 0.1% formic acid-water solution, and the liquid phase B was 0.1% formic acid-acetonitrile solution. The chromatographic column Trap column, 100 μm × 20 mm (RP-C18, Agilent) was equilibrated with 100% A at 3 μL / min. The sample was loaded by an automatic sampler and combined with the Trap column, and then separated by an Analysis column, 75 μm × 250 mm (RP-C18, New Objective, USA) chromatographic column at a flow rate of 300 nL / min.

[0030] The enzymatic product was separated by capillary high performance liquid chromatography and then analyzed by mass spectrometry using a Q-Exactive plus mass spectrometer (ThermoFisher Scientific). The detection method: calibrated by standard calibration liquid before use, parent ion scanning range: 300-1500 m / z, mass spectrometry scanning mode: information dependent acquisition mode (DDA, Data Dependent Acquisition), 20 strongest fragment maps (MS2 scan) were collected after each full scan, fragmentation mode: high energy collision dissociation (HCD, high energy collision dissociation), NCE energy 28, dynamic exclusion time: 25 s. The resolution of MS1 at M / Z 200 was 70000, the AGC target was set to 3e6, the maximum injection time was 100 ms, the resolution of MS2 was set to 17500, the AGC target was set to 1e5, and the maximum injection time was 50 ms. Based on the Raw file obtained by mass spectrometry detection, Proteome Discoverer 2.5 was used to search the database (uniprotkb_Cicadidae_2024_09_09), and then protein identification was performed based on the database search results.

[0031] The total ion current chromatogram (TIC) Figure 3 ) shows that the peptide segment with a retention time of 36 min has the highest content, and 1004 protein sequences are identified by database search, of which a total of 963 have high reliability. Of the 963 identified proteins, only 6 belong to the annotated Cryptotympana atrata proteome of Uniprot, and the remaining 957 are identified by homology with closely related species in the Uniprot database of the suborder Cicadoidea ( Cicadomorpha ).

[0032] 3. Bioinformatics prediction of sensitization results The sensitization potential of 963 protein sequences identified by LC-MS / MS was predicted using AllerCatPro 2.0. The results are as follows: Figure 4 As shown in Tables 1-2, 241 protein sequences may have potential sensitization. The allergens corresponding to these 241 protein sequences originate from 51 species, including animals, plants, and fungi. The proteins with potential sensitization mainly include tropomyosin, peroxidase, carboxylesterase, serine carboxypeptidase, filament protein A, and troponin. Further sequence homology comparisons with known allergens in the Allermatch (https: / / allermatch.org / allermatchsearch / form) and AllergenOnline (http: / / www.allergenonline.org / ) databases, based on amino acid sequence homology greater than or equal to 35%, identified potential allergens in cicada pupa proteins including tropomyosin, arginine kinase, heat shock protein 70, glyceraldehyde-3-phosphate dehydrogenase, enolase, ribosomal small subunit protein RACK1, and tubulin α subunit.

[0033] Table 1 AllergenOnline matches (≥35% identity, 80-aa window) Table 2. Prediction results of AllerCatPro2.0 and Allermatch™ 4. Cross-reactivity assessment According to official statistics from WHO / IUIS (http: / / www.allergen.org), over 340 allergen proteins from arthropods (insects, crustaceans, etc.) are currently included. Among these, food allergens include over 50 types such as tropomyosin, arginine kinase, triose phosphate isomerase, troponin I, and filamentin C, primarily from silkworms. Bombyx mori ), cross crab ( Charybdis feriata ), Beihai shrimp ( Crangon crangon ), tiger prawn ( Penaeus monodon ), and the blue crab that burrows ( Scylla paramamosain The proteins tropomyosin and arginine kinase identified by mass spectrometry belonging to cicada pupae were compared with homologous allergen proteins from other species included in the WHO / IUIS Allergen Nomenclature Database using multiple amino acid sequence alignment analysis, and a phylogenetic tree was constructed.

[0034] like Figures 5-8 As shown, the results indicate that the amino acid sequence identity of the cicada pupa allergen tropomyosin with all homologous tropomyosin allergens is over 80%. For example, the sequence identity with the silkworm (Bombyx mori) allergen component Bomb m3 (92.96%), the cross crab (Charybdis feriata) allergen component Cha f1 (86.74%), and several crustacean allergen components Cra c1, Exom 1, Homa 1, Lit v 1, Mac r 1, Mel l 1, Mete 1, Pan b1, Pan s 1, Pen a 1, Pen i 1, Pen m 1, Por p 1, Pro c 1, and Scy p is 83.45%–84.86%. The amino acid sequence identity of the cicada pupa allergen arginine kinase with all homologous arginine kinase allergens, except for giant freshwater shrimp (… Macrobrachium rosenbergii Except for the allergen component Mac r2, which was 42.52%, all others were above 80%, similar to silkworm (Mac r2). Bombyx mori The amino acid sequence identity of the allergen component Bomb m1 was 84.23%, while the sequence identity of other crustacean allergen components Cal b2, Cra c2, Lit v2, Pen m2, Pro c2, and Scy p2 ranged from 81.97% to 83.38%. Phylogenetic analysis showed that the tropomyosin and arginine kinase, allergens from cicada pupae, were distributed in the same clade as the allergen components from silkworms in their evolutionary relationship with homologous allergens from other species.

[0035] The final allergens identified through screening were: The sequence of cicada tropomyosin is: MDAIKKKMQAMKLEKDNAMDRALLCEQQARDANLRAEKAEEEARALQKKIQTIENDLDQTQEQLMQVNAKLEEKDKALQNAESEVAALNRRIQLLEEDLERSEERLATATAKLAEASQAADESERARKVLENRSLADEERMDALENQLKEARFLAEEADKKYDEVARKLAMVEADLERAEERAETGESKIVELEEELRVVGNNLKSLEVSEEKANQREEEYKNQIKTLTTRLKEAEARAEFAERSVQKLQKEVDRLEDELVVEKERYRVIGDGLDLAFVELYGY; Arginine kinase, the sequence of which is: MVDAATLEKLEAGFKKLKASNSKSLLKKYLTQEVFDKLKTLKTPTFGSTLLDVIQSGLENHDSGVGIYAPDAEAYTIFADLFDPIIEDYHGGFKKTDKHPAKDWGDVGSLANLDPAGEYIISTRVRCGRSMEGYPFNPCLTEAQYKEMEGKVSSTLSGLSGELKGNFYPLTGMTKEVQQKLIDDHFLFKEGDRFLQAANACRFWPSGRGIYHNENKTFLVWCNEEDHLRLISMQMGGDLGQVYRRLVTAVNDIEKRIPFSHNDRLGFLTFCPTNLGTTVRASVHIKVPKLAANRAKLEEIACKFNLQVRGTRGEHTEAEGGVYDISNKRRMGLTEYQAVKEMNDGIAELIKIEREL.

[0036] Table 3 Arginine kinases of arthropod allergens known in WHO / IUIS Table 4 Troponins of arthropod allergens known in WHO / IUIS

Claims

1. A proteomic and bioinformatic based animal derived novel food resource allergen, characterized in that, The allergen is a crustacean tropomyosin or arginine kinase; The crustacean tropomyosin is cicada tropomyosin, and its sequence is: MDAIKKKMQAMKLEKDNAMDRALLCEQQARDANLRAEKAEEEARALQKKIQTIENDLDQTQEQLMQVNAKLEEKDKALQNAESEVAALNRRIQLLEEDLERSEERLATATAKLAEASQAADESERARKVLENRSLADEERMDALENQLKEARFLAEEADKKYDEVARKLAMVEADLERAEERAETGESKIVELEEELRVVGNNLKSLEVSEEKANQREEEYKNQIKTLTTRLKEAEARAEFAERSVQKLQKEVDRLEDELVVEKERYRVIGDGLDLAFVELYGY; The arginine kinase is cicada arginine kinase, and its sequence is: MVDAATLEKLEAGFKKLKASNSKSLLKKYLTQEVFDKLKTLKTPTFGSTLLDVIQSGLENHDSGVGIYAPDAEAYTIFADLFDPIIEDYHGGFKKTDKHPAKDWGDVGSLANLDPAGEYIISTRVRCGRSMEGYPFNPCLTEAQYKEMEGKVSSTLSGLSGELKGNFYPLTGMTKEVQQKLIDDHFLFKEGDRFLQAANACRFWPSGRGIYHNENKTFLVWCNEEDHLRLISMQMGGDLGQVYRRLVTAVNDIEKRIPFSHNDRLGFLTFCPTNLGTTVRASVHIKVPKLAANRAKLEEIACKFNLQVRGTRGEHTEAEGGVYDISNKRRMGLTEYQAVKEMNDGIAELIKIEREL.

2. The method for identifying allergens and evaluating potential allergenicity of animal-derived new food resources based on proteomics and bioinformatics according to claim 1, characterized in that, The method comprises the following steps: (1) First, extract, separate and identify the proteins of the animal-derived new food resources; (2) According to the LC-MS / MS identification, obtain the protein information belonging to the target food resources and the protein amino acid sequence; (3) Input the protein amino acid sequence into an online allergen prediction platform for prediction, screen out the proteins with higher sensitization risk, and perform homology comparison with the known allergens in the database, and define the proteins with higher possibility of sensitization simultaneously predicted by at least two platforms as potential allergens in the target food resources; (4) Perform multiple sequence alignment on the protein sequences of the predicted potential allergens and the corresponding proteins of other arthropod known allergens consistent with the LC-MS / MS identification results, obtain a phylogenetic tree, identify the homologous regions thereof with common allergens, find protein sequences with closer genetic relationship, and evaluate the cross-reactivity.

3. The method of claim 2, wherein, In step (1), when the extracted sample is a polypeptide sample, the desalted polypeptide sample is dissolved in 0.1% formic acid-water solution and separated by a Nano-HPLC liquid phase system UltiMate 3000RSLCnano (ThermoFisher Scientific); the liquid phase A is 0.1% formic acid-water solution, and the liquid phase B is 0.1% formic acid-acetonitrile solution; the chromatographic column is Trap column, 100 μm × 20 mm (RP-C18, Agilent), and the column is balanced at 100% A liquid 3 μL / min; the sample is loaded by an automatic sampler and combined with the Trap column, and then separated by an Analysis column, 75 μm × 250 mm (RP-C18, New Objective, USA) chromatographic column, at a flow rate of 300 nL / min; After the enzymatic product is separated by capillary high performance liquid chromatography, mass spectrometry is performed by a Q-Exactive plus mass spectrometer (ThermoFisher Scientific); the detection mode is calibrated by a standard calibration liquid before use, the parent ion scanning range is 300-1500 m / z, the mass spectrometry scanning mode is information-dependent acquisition mode (DDA, Data Dependent Acquisition), 20 strongest fragment maps (MS2 scan) are collected after each full scan, the fragmentation mode is high energy collision dissociation (HCD, high energy collision dissociation), NCE energy is 28, dynamic exclusion time is 25 s; the MS1 resolution is 70000 at M / Z 200, the AGC target is set to 3e6, the maximum injection time is 100 ms, the MS2 resolution is set to 17500, the AGC target is set to 1e5, and the maximum injection time is 50 ms; based on the Raw file obtained by mass spectrometry detection, Proteome Discoverer 2.5 is used to search in the database, and then protein identification is performed based on the database search results.

4. The method of claim 2, wherein, In step (2), the protein amino acid sequence is searched and downloaded from the UniprotKB database with the ID of the protein as the index.

5. The method according to any one of claims 2-4, characterized in that, In step (3), the online allergen prediction platform is AllerCatPro2.0; the database is AllerCatPro2.0, AllergenOnline and Allermatch™.

6. The method of claim 2, wherein, In step (4), the multiple sequence comparison uses the ClustalOmega tool.

7. The method according to claim 2 or 6, characterized in that, In step (4), the phylogenetic tree is obtained by using the BLOSUM62 algorithm.

8. The method of claim 2, 6 or 7, wherein, In step (4), the common allergens include but are not limited to crustacean tropomyosin and arginine kinase.

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