|Year : 2021 | Volume
| Issue : 2 | Page : 72-81
In silico prediction of B-cell and T-cell epitope of Ves g 5 and Vesp m 5 allergens
Kuldeepkumar Singh1, Tushar T Khandagale1, Akshita Puri2, Sangeeta Sinha1
1 Department of Zoology, Nowrosjee Wadia College, Pune, India
2 Department of Zoology, Post Graduate Teaching, R.T.M Nagpur University, Nagpur, Maharashtra, India
|Date of Submission||25-Jan-2022|
|Date of Acceptance||13-May-2022|
|Date of Web Publication||08-Jul-2022|
Department of Zoology, R.T.M Nagpur University, Nagpur, Maharashtra
Dr. Sangeeta Sinha
Department of Zoology, Nowrosjee Wadia College, Pune, Maharashtra
Source of Support: None, Conflict of Interest: None
BACKGROUND: Hymenoptera venom allergy is one of the leading insect allergies in which allergens that promote IgE-mediated immune responses cause mild-to-severe reactions. Vespula germanica has a high spread rate and invades many regions of the world, while Vespa mandarinia has caused many fatalities in Asian countries including India. Ves g 5 and Vesp m 5 are important allergens of V. germanica and V. mandarinia, respectively, which cause allergenic reactions.
AIM: This study aimed to predict the B- and T-cell epitopes of allergens Ves g 5 and Vesp m 5 using computational tools.
MATERIALS AND METHODS: ProtParam, Jalview, and Swiss-Model analyzed the physiochemical, allergen sequence and 3D model. BepiPred-2.0, ABCPred, and ElliPro predicted B-cell epitopes, while Immune Epitope Database major histocompatibility complex-II binding prediction tool and CD4+ T-cell immunogenicity prediction tool were used to predict and confirm immunogenic T-cell epitopes
RESULTS: Nine linear and four discontinuous B-cell epitopes were predicted for the Ves g 5 allergen and ten linear and five discontinuous B-cell epitopes were predicted for the Vesp m 5 allergen. Four and three T-cell epitopes were predicted for the allergens Ves g 5 and Vesp m 5, showing efficient binding and immunogenicity, respectively.
CONCLUSION: Venom immunotherapy used as a treatment for HVA shows few fatal reactions and side effects, hence epitope-based vaccines or therapies are necessary. These results can be further used in the process of better immunotherapy and peptide-based vaccine design as well as to understand the etiology of Ves g 5 and Vesp m 5 allergens.
Keywords: Allergen, antigen 5, epitope prediction, in silico, Vespa mandarinia, Vespula germanica
|How to cite this article:|
Singh K, Khandagale TT, Puri A, Sinha S. In silico prediction of B-cell and T-cell epitope of Ves g 5 and Vesp m 5 allergens. Indian J Allergy Asthma Immunol 2021;35:72-81
|How to cite this URL:|
Singh K, Khandagale TT, Puri A, Sinha S. In silico prediction of B-cell and T-cell epitope of Ves g 5 and Vesp m 5 allergens. Indian J Allergy Asthma Immunol [serial online] 2021 [cited 2022 Aug 8];35:72-81. Available from: https://www.ijaai.in/text.asp?2021/35/2/72/350078
| Introduction|| |
Allergic diseases are hypersensitivity reactions mediated by immunoglobulin E (IgE). Exposure to allergens promotes an IgE-mediated immune response. Genetic and environmental problems increase the risk of developing allergic diseases. Specific IgE antibodies react with the allergens to trigger allergic reactions. In Germany, allergic sensitization has been detected in approximately 50% of the population. An insect venom allergy leads to pain, itching, dizziness, rapid pulse, swelling of the airways and throat, severe asthma, and anaphylaxis.
Hymenoptera venom allergy (HVA) causes rarely fatalities with mild-to-severe systemic reactions (SSRs) which leads to multi-organ failure., Patients experiencing a large local reaction have a 5%–10% risk of developing SSR to a sting in future. Hymenoptera stings are so common that 56.6%–94.5% of the general population has been stung at least once in their lifetime and has caused about 20% of deaths worldwide., Among hymenopterans, honey bees and yellowjacket wasps are major sources of venom allergies and yellowjacket venom has three main allergens which are phospholipase A1, antigen 5 (Ag5), and hyaluronidase.
Vespula germanica (German wasp) is a eusocial vespid that has spread and invaded many regions of the world in the last century. It shows the highest spread rate of 37 km/year among other hymenopterans., Factors that enhance the successful invasion of these social insects include their high reproductive rates, ability to adapt, and their eusocial nature. Among vespids, Ag5 (like Ves g 5 and Vesp m 5) is a potent and has been reported as the most allergenic venom component, while other allergens like hyaluronidase has been considered minor vespid allergen., Ag5 proteins belong to the CAP superfamily (cysteine-rich secretory proteins, antigen 5, and pathogenesis-related proteins). Hornets are the largest eusocial wasp. A rare case of death due to multiple hornet bites in Burdwan (West Bengal) was reported. Two fatal cases due to multiple stings by V. mandarinia have been reported in Tamil Nadu (India), and subsequently, one patient developed acute encephalopathy. Approximately 30–50 persons show fatalities each year due to Vespa mandarinia in Japan, while it caused 42 deaths and 1675 injuries in China in 4 months in 2013.,
The risk of anaphylaxis to the stings of Vespula (V. germanica and Vespula vulgaris) and Vespa is higher in adults than in children because of increased sting exposure, comorbidities, and concomitant medication use in adults. Venom immunotherapy (VIT) is used as a treatment for patients allergic to HVA. VIT has shown some fatal reactions, mortality and few side effects, ineffectiveness, and in some cases, the risk of serious SSR. Elucidation of B- and T-cell epitopes of allergens may enhance the treatment of these allergies. B-cell epitopes can be used for the diagnosis and development of vaccines for immunotherapy. Various computational techniques and tools can be used to identify epitopes. T-cell epitope prediction helps identify the shortest peptides in an antigen that stimulates CD4 and CD8 T-cells. The main objective of epitope identification is to replace an antigen with antigenic peptides with better binding affinity and immunogenicity for immunization, antibody production, and serodiagnosis.
In the present study, we identified and predicted the B-cell and T-cell epitopes of the allergen Ves g 5 and Vesp m 5 using an in silico approach [Figure 1]. The results of the B- and T-cell epitope can be used in peptide-based vaccine design for venom allergy in both species after experimental validation.
|Figure 1: Schematic diagram of epitope prediction of allergens Ves g 5 and Vesp m 5. It summarizes the strategies used for the selection and prediction of B-cell and T-cell epitope of Ves g 5 and Vesp m 5 allergens using various in silico tools and methods|
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| Materials and Methods|| |
The primary sequence was retrieved for Ves g 5 with its homologs (BLAST-https://blast.ncbi.nlm.nih.gov/Blast.cgi/) and Vesp m 5 from the protein database of NCBI (http://www.ncbi.nlm.nih.gov/) in FASTA format with the Accession numbers P35784.1 (Ves g 5) and P81657.1 (Vesp m 5).
The physiochemical characteristics of allergens such as molecular weight, the number of amino acids, theoretical isoelectric point (pI), amino acid composition, instability index (II), aliphatic index, and grand average of hydropathicity (GRAVY) were determined using the ProtParam tool to determine stability, thermostability, and hydrophilicity (http://web.expasy.org/protparam/).
Sequence analysis and visualization
Three allergen 5 sequences Ves g 5 (accession no. P35784.1), Ves v 5 (accession no. Q05110.1), and Vesp m 5 (accession no. P81657.1) were retrieved from the NCBI protein database. The sequences were aligned using Clustal X. The alignment file was visualized and analyzed to study the conservation, consensus, quality, and occupancy annotations using Jalview 220.127.116.11 software.
Homology modeling and validation
Homology modeling was performed using the Swiss-Model (https://swissmodel.expasy.org/). BLAST and HHblits template searches were performed using the Swiss-Model Template Library to find suitable templates for Ves g 5 (accession no. P35784.1) and Vesp m 5 (accession no. P81657.1). The template considered was based on a high score, low E-value, and maximum sequence identity. For both allergens, 1.qnx. 1, A template (V. vulgaris: Ves v 5 allergen) was selected.
The 3D structure model of the Ves g 5 and Vesp m 5 allergen proteins was further validated using VERIFY-3D, ERRAT, QMEAN, and Ramachandran plots. The structural model was generated and evaluated using a Ramachandran plot with a PDBsum server. The structure formed was used for Epitope Prediction.
B-cell epitope prediction
Linear sequences and different physicochemical parameters, such as the Emini surface accessibility prediction method, Kolaskar and Tongaonkar antigenicity prediction method, Chou and Fasman beta-turn prediction method, Karplus and Schulz flexibility prediction method, and Parker hydrophilicity prediction tool, were correlated to obtain B-cell epitopes of Ves g 5 and Vesp m 5.
In this study, three methods were used to predict B-cell epitopes: BepiPred-2.0, ABCPred, and ElliPro. BepiPred linear epitope prediction (BepiPred-2.0) (http://tools.iedb.org/bcell/) determines the actual linear B-cell epitope based on physicochemical parameters for Ves g 5 and Vesp m 5 allergens with a default threshold of 0.5. The ABCPred server (http://www.imtech.res.in/raghava/abcpred/) was used to predict B-cell epitopes using a fixed-length pattern. The window length used for the prediction with 0.80 thresholds was set as 16-mer. The structure-based method ElliPro (http://tools.iedb.org/ellipro/) predicted continuous and discontinuous B-cell epitopes for both allergens based on their 3D structure. Potential epitope sequences were confirmed by combining all predicted epitopes of all methods.
T-cell epitope prediction
Prediction of T-cell epitopes interacting with major histocompatibility complex (MHC) class II was accessed by the Immune Epitope Database (IEDB) MHC-II binding prediction tool for CD4+ cells.,,,, IEDB Recommended 2.2 method was used which uses the consensus approach, combining NN-align, SMM-align, CombLib, and Sturniolo or NetMHCIIpan. The human allele reference set was used to determine the interaction between the T-cell epitope and the MHC-II allele. 15 amino acid length of the peptide or epitope was fixed. The predicted output was given in units of IC50 nM for the combinatorial library and SMM align. Therefore, a lower number indicates higher affinity. CD4+ T-cell immunogenicity prediction tool from the IEDB server was used to get the immunogenicity score.
| Results and Discussion|| |
Physiochemical analysis of allergens revealed that Ves g 5 and Vesp m 5 have a molecular weight of 23329.59 Da and 22546.67 Da, respectively, which is more than 20,000 Da which clears one of the criteria of being an immunogenic sequence. At pI, proteins are stable and compact. The theoretical pI for both the allergens indicated that they are basic in nature. The aliphatic index (AI) is a factor which states the thermostability of protein. The AI values for both the allergens state that they have good thermostability indicating the stability of these allergens under a wide temperature range. The II determines the stability of allergen and the protein having value <40 is stable and more than 40 is considered unstable. The II value for both the allergens is <40, hence these allergens are very much stable. Structural stability is essential for proteins for the presentation of antigenic peptides on MHC, which may play an important role in triggering strong immune reactions. GRAVY value for Ves g 5 is −0.837 and for Vesp m 5 is −0.621 which makes it hydrophilic, indicating the possibility of better interaction with water. The GRAVY value was calculated as the sum of hydropathy values of all amino acids, divided by a number of residues in sequence [Table 1]., Therefore, profiling of these protein sequences using physiochemical analysis clearly marks them as immunogenic and makes them a suitable vaccine candidate capable of triggering an immune response.
Sequence analysis and visualization
In [Figure 2], conserved annotation indicates that the alignment sequence has many conserved regions indicating conserved functional parts. In consensus annotation histogram, there are very few gaps present and there are very few modal values which are shared by more than 1 residue. The presence of eight conserved cysteine residues makes the protein sequence more stable by forming disulfide bonds which help the protein to maintain its structure which is further necessary for interaction with the molecule. Quality annotation suggests that there are no or fewer mutations. These features specify that the sequences may share similar epitopes. Domain regions are similar among the allergens which indicate of having similar functions [Figure 2], which was confirmed using the UniProt server. Ves g 5 and Vesp m 5 belong to the CAP domain and CAP superfamily with having a function of eliciting strong allergenic responses. Hymenopteran Ag5 shows various biological processes such as reproduction, cancer, and immune regulation.
|Figure 2: Allergen Ves g 5, Vesp m 5, and Ves v 5 sequence analysis and visualization using Jalview software. (a) Conservation, quality, consensus, and occupancy tracks from Jalview for allergen Ves g 5, Vesp m 5, and Ves v 5 alignment sequences are shown. The conservation annotation marks conserved residues (score 11) with a yellow asterisk “*”, and columns that have (score 10) with a yellow “+”; less conserved positions are shown in darker colors with decreasing score. Quality and occupancy annotations are also represented in histogram form. The consensus annotation denotes nonconserved residues as '+' and '−' denotes gap residues. (b) Sequence identity matrix between Vespula germanica, Vespula vulgaris, and Vespa mandarinia is mentioned in percentage|
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Homology modeling and validation
The 3D structure model was predicted using the Swiss-Model for Ves g 5 [Figure 3]a and Vesp m 5 [Figure 4]a. The homology modeling template, 1.qnx.1.A (Ves v 5), has a high sequence identity of 93.63% with Ves g 5 and 65.67% with Vesp m 5. The 3D structure may help further to understand the protein function and its interaction with ligand and other proteins.
|Figure 3: Homology modeling and model evaluation of allergen Ves g 5. (a) 3D structure model predicted by Swiss-Model server for Ves g 5 allergen protein using 1.qnx. 1, A template (Vespula vulgaris-Ves v 5 allergen). (b) Ramachandran plot of the final 3D structure of Ves g 5 allergen protein was predicted using PDBsum server. 89.9% were in most favored region whereas 9.6% were in additional allowed region and 0.6% are in disallowed region. (c) Local quality estimate plot scores the residue of model Ves g 5 showing expected similarity with target structure. (d) This plot determines the quality of model for Ves g 5 which is acceptable as it lies in range 0–4.0|
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|Figure 4: Homology modeling and model evaluation of allergen Vesp m 5. (a) 3D model predicted for allergen Vesp m 5 using Swiss-Model server using 1.qnx. 1, A template (Vespula vulgaris-Ves v 5 allergen). (b) Ramachandran plot of the final 3D structure of Vesp m 5 allergen protein was predicted using PDBsum server. 89.1% were in most favored region whereas 10.3% were in additional allowed region and 0.6% are in disallowed region. (c) Local quality estimate plot scores the residue of model Vesp m 5 showing expected similarity with target structure. (d) This plot determines the quality of model for Vesp m 5 which is acceptable as it lies in range 0–4.0|
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The Ramachandran plot of the 3D structure showed that 89.9% and 89.1% amino acid residues for Ves g 5 [Figure 3]b and Vesp m 5 [Figure 4]b, respectively, were within the most favored regions [A, B, L], while 9.6% for Ves g 5 and 10.3% for Vesp m 5 of residues were in the additional allowed region [a, b, l, p], whereas the amino acid residues in the disallowed region were 0.6% for the 3D structure of both the allergens [~a, ~b, ~l, ~p]. Hence, total of 99.5% (Ves g 5) and 99.4% (Vesp m 5) amino acid residues were in favored and allowed regions, showing that amino acid distribution is reasonable which makes the quality of the models reliable and it gives more confidence in the structural conformation for targeted epitopes.
In [Figure 3]c and [Figure 4]c, a local quality estimate plot scored the residue of model showing expected similarity with target structure where most of the residues show some similarity with the target structure. [Figure 3]d and [Figure 4]d determine that the quality of the model for Ves g 5 and Vesp m 5 is in the range 0–4.0, which signifies that the model is quite similar to the structure of a template. For the 3D model of Ves g 5 and Vesp m 5, the ERRAT program showed that the overall quality factor was 95.65% and 90.60%, respectively. VERIFY-3D program revealed that 89.22% (Ves g 5) and 92.57% (Vesp m 5) residues had an averaged 3D-1D score ≥0.2. The QMEAN value for Ves g 5 3D model is −0.80 and for Vesp m 5 3D model is −1.35 which is close to 0 and not <−4.0. These values validate both the models to be structurally correct and acceptable. ERRAT confirms good quality for both the 3D models and VERIFY-3D measures the compatibility of any protein structure with its amino acid sequence, hence Ves g 5 and Vesp m 5 3D models are compatible with its sequence. The 3D structure formed was used in the ElliPro tool to determine B-cell epitope.
B-cell epitope prediction
Sequence-based B-cell epitope prediction
Using a multi-method analysis improves the accuracy of epitope prediction. The Emini surface accessibility method finds the hydrophilic regions that are generally exposed on the surface. The minimum and maximum surface accessibility values were 0.063 and 5.979, respectively, for Ves g 5 [Figure 5]a and 0.078 and 3.701, respectively, for Vesp m 5 [Figure 6]a. Few regions show high surface accessibility properties in both allergens and hence these residues may help in stimulating immune response. Kolaskar and Tongaonkar antigenicity prediction tool analyzed the antigenicity property of both the allergens. The maximum and minimum antigenic values were 1.192 and 0.870, respectively, for Ves g 5 [Figure 5]b and 0.910 and 1.184, respectively, for Vesp m 5 [Figure 6]b. It specifies that most of the regions are antigenic and may consist of an effective epitope. Chou and Fasman beta-turn prediction tool predicted beta-turn region. The beta-turn region has a vital role in antigenicity as most linear epitopes are present in these regions and both the allergens have beta-turn regions having the ability to induce antigenicity. The average minimum and maximum beta-turn scores for Ves g 5 were 0.714 and 1.476, respectively [Figure 5]c, while for Vesp m 5 were 0.686 and 1.381, respectively [Figure 6]c. Karplus and Schulz flexibility prediction method predicted minimum and maximum flexibility values of 0.892 and 1.113, respectively, for Ves g 5 [Figure 5]d and 0.887 and 1.101, respectively, for Vesp m 5 [Figure 6]d. Most of the residues of both the allergens are flexible. Experimental data have proved that residues interacting with an antibody are mostly flexible which will help in initiating immune response. Parker hydrophilicity prediction tool estimated the hydrophilicity for Ves g 5 with minimum (−2.771) and maximum (6.629) scores [Figure 5]e. For Vesp m 5, the maximum and minimum scores were 5.200 and −2.343, respectively [Figure 6]e. The regions showing hydrophilic properties are suitable for ligand binding. All the predicted peptide residues expressing these properties are summarized in [Table S1[Additional file 1]]. The higher the presence of these properties, more is the chance of forming an epitope.
|Figure 5: Graphical presentation of B-cell epitope for allergen Ves g 5 using IEDB software. The x-axis and y-axis represent the position and score, respectively, and yellow region above the threshold value expresses the properties mentioned below. (a) Emini surface accessibility prediction (threshold: 1.0). (b) Kolaskar and Tongaonkar antigenicity prediction (threshold: 1.023). (c) Chou and Fasman beta-turn prediction (threshold: 1.029). (d) Karplus and Schulz flexibility prediction (threshold: 1.004). (e) Parker hydrophilicity prediction (threshold: 2.105). (f) BepiPred linear B-cell epitope prediction (threshold: 0.5). The highest peak and yellow region indicates the most potent epitope. (g) ElliPro-based graphical presentation of epitopes for Ves g 5. IEDB: Immune Epitope Database|
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|Figure 6: Graphical presentation of B-cell epitope for allergen Vesp m 5 using IEDB software. The x-axis and y-axis represent the position and score, respectively, and yellow region above the threshold value expresses the properties mentioned below. (a) Emini surface accessibility prediction (threshold: 1.0). (b) Kolaskar and Tongaonkar antigenicity prediction (threshold: 1.019). (c) Chou and Fasman beta-turn prediction (threshold: 1.019). (d) Karplus and Schulz flexibility prediction (threshold: 0.997). (e) Parker hydrophilicity prediction (threshold: 2.025). (f) BepiPred Linear B-cell epitope prediction (threshold: 0.5). The highest peak and yellow region indicates the most potent epitope. (g) ElliPro-based graphical presentation of epitopes for Vesp m 5. IEDB: Immune Epitope Database|
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The BepiPred graph visualized the epitope-based region which signifies that the major region can form a potent epitope [Figure 5]f and [Figure 6]f. For the Ves g 5 and Vesp m 5 sequence, a total of 8 and 10 main epitopes were predicted, respectively. ABCPred predicted 9 epitopes for Ves g 5 and 11 epitopes for Vesp m 5. All the top epitopes were ranked based on their score [Table S2[Additional file 2]] and [Table S3[Additional file 3]].
Structure-based B-cell epitope prediction
ElliPro was provided with the 3D structure of both the allergens to get epitopes as output. For Ves g 5, it predicted 7 linear and 4 discontinuous epitopes [Table S2] and [Table S4[Additional file 4]]. For Vesp m 5, it predicted 7 linear and 5 discontinuous epitopes [Table S3] and [Table S4]. The epitope regions are presented on the graph [Figure 5]g and [Figure 6]g.
Final predicted epitope
The linear B-cell epitope predicted for both the allergens from BepiPred 2.0, ABCPred, and ElliPro was compared with each other and the final epitope was summarized [Table 2]. For Ves g 5, the final 9 linear and 4 discontinuous epitopes were predicted, and for Vesp m 5, 10 linear and 5 discontinuous epitopes were predicted. Both Ves g 5 and Vesp m 5 allergens were compared with each other and were found that Ves g 5 (peptide: 5–10, 53–77, and 98–107) shares cross-reactive epitopes with Vesp m 5 (peptide: 5–11, 53–57, 61–76, and 96–107) [Figure S1[Additional file 5]] and [Table 2]. Cross-reactive epitope may be useful in reducing the number of allergen without disturbing the efficacy of therapy. Predicted B-cell epitopes have all the properties to be immunogenic and may start a strong immune response or reaction.
T-cell epitope prediction
T-cell epitope is essential for the initiation of immune responses in our body. For the prediction of CD4+ T-cell epitopes, the protein sequence of Ves g 5 and Vesp m 5 was provided to IEDB MHC-II binding prediction tool. MHC class II molecules present epitopes to CD4+ T-cells only, which further leads to immune response, and MHC class II binding prediction data are widely used to identify epitope candidates in allergens. MHC class II has three human leukocyte antigen (HLA) alleles HLA-DP, HLA-DQ, and HLA-DR. A very few binding data are available for HLA-DP, as it is less diverse and more difficult to work experimentally, hence HLA-DR allele has been studied majorly., HLA-DR and HLA-DQ were majorly considered for the epitope prediction. A small numbered percentile rank indicates high affinity and the small adjusted rank indicated the high binding sequence. The peptides having percentile rank and adjusted rank less than the threshold 2.0 were considered an epitope. For Ves g 5 and Vesp m 5, the top 16 and 13 peptide sequences were predicted, respectively [Table 3] and [Table 4]. The predicted T-cell epitope is important in B-cell activation, class switching, and allergic reactions.
After comparing the peptides with a good binding score and immunogenicity score, the better epitope was chosen [Table 3] and [Table 4]. For Ves g 5, KYESLKPNCANKKVV, YESLKPNCANKKVVA, FLKIGHYTQMVWANT, and LKIGHYTQMVWANTK and, for Vesp m 5, EVGHYTQMVWAKTKE, AKSMNTLVWNDELAQ, and IEVGHYTQMVWAKTK were considered the well-scored epitope. The predicted epitopes of Ves v 5 by Bohle et al. were compared and validated with these T-cell epitopes of Ves g 5 and Vesp m 5 and all the Ves g 5 epitopes were found to be cross-reactive with Ves v 5 epitopes [Figure S2[Additional file 6]]. Due to this cross-reactivity, differentiation of allergies to different vespid species becomes challenging. The epitope can be used in further studies and should be confirmed using an experimental approach.
| Conclusion|| |
This study predicted the immunogenic linear and discontinuous B-cell epitopes and T-cell epitopes for Ves g 5 and Vesp m 5 allergens. Three B-cell epitopes of Ves g 5 are cross-reactive with Vesp m 5 epitopes, while all the four Ves g 5 T-cell epitopes were found to be cross-reactive with Ves v 5 epitopes. However, the accuracy of these epitopes requires confirmation in further experiments. It could be used for the development of better peptide allergen-specific immunotherapy after experimental validation which will be convenient for the patient, and instead of using whole protein or venom, only a small amount of allergen derivatives will be required and it will also help in reducing the time for the treatment.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ahmadi F, Dorosti H, Ghasemi Y, Nezafat N. In silico
design of epitope-based allergy vaccine against Bellatella germanica
cockroach allergens. Int J Pept Res Ther 2020;26:1739-49.
Emiliani Y, Sánchez A, Munera M, Sánchez J, Aparicio D. In silico
analysis of cross reactivity among phospholipases from Hymenoptera species. F1000Res 2021;10:2.
Bergmann KC, Heinrich J, Niemann H. Current status of allergy prevalence in Germany: Position paper of the Environmental Medicine Commission of the Robert Koch Institute. Allergo J Int 2016;25:6-10.
Mohd Adnan K. A review on Respiratory allergy caused by insects. Bioinformation 2018;14:540-53.
Perez-Riverol A, Palma MS, Jakob T. Current challenges in molecular diagnostics of insect venom allergy. Allergo J Int 2020;29:79-91.
Macchia D, Cortellini G, Mauro M, Meucci E, Quercia O, Manfredi M, et al. Vespa crabro
immunotherapy versus Vespula
-venom immunotherapy in Vespa crabro
allergy: A comparison study in field re-stings. World Allergy Organ J 2018;11:3.
Ludman SW, Boyle RJ. Stinging insect allergy: Current perspectives on venom immunotherapy. J Asthma Allergy 2015;8:75-86.
Blank S, Haemmerle S, Jaeger T, Russkamp D, Ring J, Schmidt-Weber CB, et al.
Prevalence of Hymenoptera venom allergy and sensitization in the population-representative German KORA cohort. Allergo J Int 2019;28:183-91.
Ollert M, Blank S. Anaphylaxis to insect venom allergens: Role of molecular diagnostics. Curr Allergy Asthma Rep 2015;15:26.
Masciocchi M, Martinez AS, Pereira AJ, Villacide JM, Corley JC. Dispersal behavior of yellowjacket (Vespula germanica
) queens. Insect Sci 2018;25:109-16.
Beggs JR, Brockerhoff EG, Corley JC, Kenis M, Masciocchi M, Muller F, et al.
Ecological effects and management of invasive alien Vespidae
. Biocontrol 2011;56:505-26.
Eloff J, Veldtman R, Bulgarella M, Lester PJ. Population genetics of the invasive wasp Vespula germanica
in South Africa. Insectes Soc 2020;67:229-38.
Bazon ML, Silveira LH, Simioni PU, Brochetto-Braga MR. Current advances in immunological studies on the Vespidae
venom antigen 5: Therapeutic and prophylaxis to hypersensitivity responses. Toxins (Basel) 2018;10:305.
An S, Chen L, Wei JF, Yang X, Ma D, Xu X, et al.
Purification and characterization of two new allergens from the venom of Vespa magnifica
. PLoS One 2012;7:e31920.
Blank S, Bazon ML, Grosch J, Schmidt-Weber CB, Brochetto-Braga MR, Bilò MB, et al.
Antigen 5 allergens of hymenoptera venoms and their role in diagnosis and therapy of venom allergy. Curr Allergy Asthma Rep 2020;20:58.
Chowdhuri S, Das A, Biswas S, Mukhopadhyay PP. Autopsy findings and histopathological corroboration in a case of death due to hornet bite. J Indian Acad Forensic Med 2015;37:311.
Jeyanthi KG. Fatality by multiple Asian giant hornet stings: Two case reports from south India. J South India Medicolegal Assoc 2010;2:68-71.
Yanagawa Y, Morita K, Sugiura T, Okada Y. Cutaneous hemorrhage or necrosis findings after Vespa mandarinia
(wasp) stings may predict the occurrence of multiple organ injury: A case report and review of literature. Clin Toxicol (Phila) 2007;45:803-7.
Liu Z, Li XD, Guo BH, Li Y, Zhao M, Shen HY, et al.
Acute interstitial nephritis, toxic hepatitis and toxic myocarditis following multiple Asian giant hornet stings in Shaanxi Province, China. Environ Health Prev Med 2016;21:231-6.
Dhami S, Zaman H, Varga EM, Sturm GJ, Muraro A, Akdis CA, et al.
Allergen immunotherapy for insect venom allergy: A systematic review and meta-analysis. Allergy 2017;72:342-65.
Gruzelle V, Mailhol C, Waters DW, Guilleminault L. Clinical utility of rush venom immunotherapy: Current status. J Asthma Allergy 2020;13:1-10.
Chen H, Yang HW, Wei JF, Tao AL. In silico
prediction of the T-cell and IgE-binding epitopes of Per a 6 and Bla g 6 allergens in cockroaches. Mol Med Rep 2014;10:2130-6.
Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T- and B-cell epitope prediction. J Immunol Res 2017;2017:2680160.
Potocnakova L, Bhide M, Pulzova LB. An introduction to B-cell epitope mapping and in silico
epitope prediction. J Immunol Res 2016;2016:6760830.
Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al.
The Proteomics Protocols Handbook. Totowa: Humana press; 2005. p. 571-608.
Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al.
Clustal W and Clustal X version 2.0. Bioinformatics 2007;23:2947-8.
Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview Version 2 – A multiple sequence alignment editor and analysis workbench. Bioinformatics 2009;25:1189-91.
Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger SJ, Söding J. HH-suite3 for fast remote homology detection and deep protein annotation. bioRxiv 2019;20:1-15.
Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-70.
Colovos C, Yeates TO. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511-9.
Teng F, Sun J, Yu L, Li Q, Cui Y. Homology modeling and epitope prediction of Der f 33. Braz J Med Biol Res 2018;51:e6213.
Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein Sci 2018;27:129-34.
Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 1985;55:836-9.
Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 1990;276:172-4.
Yang JT. Prediction of protein secondary structure from amino acid sequence. J Protein Chem 1996;15:185-91.
Karplus PA, Schulz GE. Prediction of chain flexibility in proteins – A tool for the selection of peptide antigens. Naturwissenschaften 1985;72:212-3.
Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 1986;25:5425-32.
Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2017;45:W24-9.
Saha S, Raghava GP. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 2006;65:40-8.
Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, et al.
ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 2008;9:514.
Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 2020;48:W449-54.
Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, et al.
Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 1999;17:555-61.
Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, et al.
Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 2010;11:568.
Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 2007;8:238.
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, et al.
Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 2018;154:394-406.
Dhanda SK, Karosiene E, Edwards L, Grifoni A, Paul S, Andreatta M, et al
. Predicting hla cD4 immunogenicity in human populations. Frontiers in Immunology, 2018;9:1-14.
Gouripur GC, Kaliwal RB, Kaliwal BB. In silico characterization of beta-galactosidase using computational tools. J Bioinform Seq Anal, 2016;8:1-11.
Hasan MA, Khan MA, Datta A, Mazumder MH, Hossain MU. A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment. Mol Immunol 2015;65:189-204.
Scheiblhofer S, Laimer J, Machado Y, Weiss R, Thalhamer J. Influence of protein fold stability on immunogenicity and its implications for vaccine design. Expert Rev Vaccines 2017;16:479-89.
Beeby M, O'Connor BD, Ryttersgaard C, Boutz DR, Perry LJ, Yeates TO. The genomics of disulfide bonding and protein stabilization in thermophiles. PLoS Biol 2005;3:e309.
Blum M, Chang HY, Chuguransky S, Grego T, Kandasaamy S, Mitchell A, et al.
The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 2021;49:D344-54.
Pan W, Chen DS, Lu YJ, Sun FF, Xu HW, Zhang YW, et al.
Bioinformatic prediction of the epitopes of Echinococcus granulosus
antigen 5. Biomed Rep 2017;6:181-7.
Petersen B, Lundegaard C, Petersen TN. NetTurnP-neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features. PLoS One 2010;5:e15079.
Bohle B, Zwölfer B, Fischer GF, Seppälä U, Kinaciyan T, Bolwig C, et al.
Characterization of the human T cell response to antigen 5 from Vespula vulgaris
(Ves v 5). Clin Exp Allergy 2005;35:367-73.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3], [Table 4]