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Allergens are proteins that may result in stimulation of a specific immune response of the body giving rise to over activation of antibody resulting in high inflammatory response leading to varied allergic reactions such as rash, itchy eyes, trouble breathing, nausea, and diarrhea (Furmonaviciene et al., 2005). The frequency of allergic disorders such as: atopic dermatitis, food allergy, allergic respiratory symptoms are on a continual and gradual rise over the last 10 years, thus becoming a severe medical concern (D’Amato, Liccardi, & D’Amato, 2000). In the purified form, allergens are mostly proteins or protein-bound in nature. Diverse sources exist that are known as reservoir and hence proper sources of allergens, which include: various food commodities, toxins and other poisonous substances, pollens, cereal flour and so on.
Lectins are the specific carbohydrate-binding proteins of non-immune or enzymatic origins with the capacity to agglutinate certain cells and/or precipitate glycoconjugates without modifying them (Dan, Liu, & Ng, 2015; He, Simpson, Sun, et al., 2015; Weis & Drickamer, 1996). It has been suggested from the highly conserved domain of lectin legume_LecRK_Arcelin_ConA region in the full-length cDNA of black turtle bean (accession number: KF601826), that the protein may belong to the phytohaemagglutinins (PHA) family (He, Simpson, Ngadi, & Ma, 2015). Black turtle bean, also known as black bean, is an important variety of common beans (Phaseolus vulgaris). The bean is high in proteins, fiber and micronutrients, and widely serves as a very popular traditional food component in tropical and subtropical countries. However, it is held to be responsible to be the cause for several food associated allergic reactions in both children and in adults at times due to the rich in lectin. Lectin poisoning occurred in Japan in 2006, at least 1,000 people had acute gastrointestinal symptoms and 100 people were hospitalized due to consumption of insufficient heating of white kidney beans (Ogawa & Date, 2014). The current scientific evidence is strong and consistent to indicate that the lectin can cause much gastrointestinal damage or disease and possible deleterious health effects via reacting with the surface epitheliums on the basis of specific carbohydrate affinity (He, Simpson, Ngadi, et al., 2015). In general, the oral acute toxicity of lectins manifests as nausea, vomiting, bloating and diarrhea in humans when they are exposed to high concentrations of the protein (Vasconcelos & Oliveira, 2004). Lectins are invariably resistant to degradation by heating and digestive enzymes, and they can bind to the surface of epithelial cells in the digestive system because of their high affinity for carbohydrates, and can result in toxic reactions with changes in intestinal permeability (Ménard, Cerf-Bensussan, & Heyman, 2010; Miyake, Tanaka, & Mcneil, 2007). Therefore, more and more food researchers are concerned with the physicochemical properties and biological characteristics of lectins.
To understand the pathogenesis of food allergy and establish effective approaches for diagnosis, treatment, and prevention, detailed information on the allergen molecule is essential. Epitopes are of particular interest to both clinical and basic biomedical researchers as they hold huge potential for vaccine design, disease prevention, diagnosis and treatment (Yang & Yu, 2009). Identification of three-dimensional structure of allergen and epitopes can be used in the better understanding of immune response to allergenic proteins, including the cross-reactivity among homologous allergens and designing of hypoallergenic variants of allergens and peptides for immunotherapy (Pom & Xe, 2010). Methods such as generation of overlapping peptides, site directed mutagenesis, T cell lines and clones, screening of libraries of overlapping peptides have been utilized for determination of B and T cell epitopes (Frazier et al., 2014). Although accurate, these methods are laborious and time consuming. With an expansion in knowledge and data about structures and epitopes of known allergens, computational algorithms are being developed. These computational tools are fast, precise and cost effective means of structure determination and mapping of antigenic determinations (Frazier et al., 2014; Yang & Yu, 2009). In-silico epitope prediction relies on computational algorithms and offers certain advantages over the traditional approaches. These methods are rapid, scalable, fairly accurate and cost effective. An increase in the number of databases and in-silico tools have greatly aided in the identification of epitopes from numerous allergens.
Both B-cell and T-cell epitopes/antigenic determinants are important in raising desired immune responses. B and T cell epitopes exhibit fundamental differences in their location allergen molecule as well as in eliciting the immunological responses (Pom & Xe, 2010). B cell epitopes can either be linear or conformational and are usually located on the surface of the allergen thereby binding readily with antibody molecules. T cell epitopes are linear antigenic peptide sequences distributed throughout the primary structure of allergen (Pomés, 2010). Nowadays, several bioinformatics and immunoinformatics tools are available for predicting B and T cell epitopes with high sensitivity and specificity, such as These tools are playing a vital role in learning the molecular basis of immunity and, notably in the development of epitope based-peptide vaccines, immunotherapy against cancer and autoimmune diseases (Frazier et al., 2014).
Identification of B and T cell epitopes of allergen will be useful in understanding the allergen antibody responses as well as aid in the development of new diagnostics and therapy regimens for lectin allergy. Therefore, the present study was undertaken to determine the molecular structure of lectin from black turtle beans (Phaseolus vulgaris) and in-silico prediction of B cell and T cell epitopes followed by their validation.
2. Materials & methods
2.1 Protein retrieval and comparative modeling
The allergen protein sequence was retrieved from NCBI protein database (https://www.ncbi.nlm.nih.gov/) using the ID: KF601826. The homologous template suitable for lectin from black turtle beans (Phaseolus vulgaris) was selected by SWISS-MODEL (https://www.swissmodel.expasy.org/). Structure template with PDB ID 3wcs.1.A having 98.43% identity was selected for the allergen protein. This template was used as a reference to determine the three – dimensional structure structures of the allergen protein. Primary structure analysis was performed using the Protparam online tool (http://web.expasy.org/protparam/). The parameters computed by ProtParam (Wilkins et al., 1999) included the molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) and the content of secondary structures were predicted by PSIPRED online tool (http://bioinf.cs.ucl.ac.uk/psipred/). N-glyoosylation and O-glyoosylation sites were analyised respectively using online sever NetNGlyc and NetOGlyc.
2.2 Stereochemical analysis and model evaluation
Structural evaluation and stereochemical analyses were performed by using various evaluation and validation tools. Backbone conformation was evaluated by analyzing the Psi/Phi Ramachandran plot obtained from PROCHECK analysis. The Ramachandran plot of the phi/psi distribution in the model is developed using PROCHECK for checking non-GLY residues at the disallowed regions. The Z-score is indicative of overall model quality and is used to check whether the input structure is within the range of scores typically found in native proteins of similar size. The Z-score was determined by PROSA web tool. The model was further evaluated through ERRAT. Furthermore, visualization of the generated model was performed using UCSF Chimera 1.10.1.
2.3 Prediction of B cell epitope
The B cell epitopes were predicted based on the physicochemical characteristics of amino acids, like hydrophobicity, surface accessibility, antigenicity, and pocket characteristics. For determination of linear epitopes, the sequence of lectin from black turtle beans (Phaseolus vulgaris) was submitted to several online prediction servers namely ABCPred (http://crdd.osdd.net/raghava//abcpred/ABC_submission.html) and BCPred (http://ailab.ist.psu.edu/bcpred/predict.html). All the predicted B-cell epitopes were checked from four properties (hydrophilicity, ﬂexibility, accessibility and antigenicity) of the peptides sequences were selected as parameters for the prediction of epitopes and epitopes showing on the surface of the membrane were selected and were subject to further analysis.
2.4 Prediction of T cell epitope
Promiscuous peptides binding to multiple HLA class I molecules were selected as T-cell epitopes. T-cell epitopes of lectin from black turtle bean (Phaseolus vulgaris) were predicted by using online immune informatics tools ProPred-I (http://crdd.osdd.net/raghava/propred1/) which predicts epitopes for 47 MHC Class-I alleles. The server cover a maximum number of HLA (Human Leukocyte antigens), therefore, are considered acceptable for predicting epitopes. Proteasome and immunoproteasome filters were set to a 5% threshold for MHC class I alleles. MHC binders that have proteosomal cleavage site at the C – terminal have greater chances to be T-cell epitopes. Furthermore, Class I immunogenicity (http://tools.iedb.org/immunogenicity/) online epitope prediction server was used for analyzing the immunogenicity of the allergen protein (Calis et al., 2013). B and T cell epitopes recognised by computational tools were mapped onto the homology modelled three dimensional structure of the allergen protein to determine their position.
2.5 Epitope conservation analysis
Epitope conservancy of the selected epitopes was tested using epitope conservancy online server (http://tools.iedb.org/conservancy/) available in IEDB analysis resource. The conservancy of each potential epitope was calculated by looking for identities in red kidney beans (Phaseolus vulgaris) and white kidney beans (Phaseolus vulgaris).
2.6 Epitope synthesis
The predicted epitopes (peptides) were synthesized by Thermo fisher Scientific, Germany with a purity of ≥ 95% and the sequences were confirmed by mass spectrometry (MALDITOF) analysis.
2.7 Enzyme linked immunosorbent assay (ELISA)
The antigenicity of the above predicted epitopes were determined by ELISA method using the 96-well microtiter plates (Govindaraj et al., 2016). After encapsulation with 1μg/100μl/well of 100 ng/100μl/well of Per a 10/B or T cell peptides/control peptide in carbonate-bicarbonate buffer (pH 9.6) and incubated overnight at 4 oC. The plates were washed with PBS twice and then blocked with 5% defatted milk (200 μl/well) for 1 h at room temperature. After washing with PBS, serial dilutions of rat IgE standard or the rat serum samples were added, respectively, for a 90 min incubation. Horseradish peroxidase (HRP)-labeled mouse-anti-rat IgE monoclonal antibody (secondary Ab) was afterward added and incubated for 60 min. Prior to the detecting, the chromogenic reaction was developed using 3,3’,5,5’-tetramethylbenzidine (TMB) for 10 min in the dark and was terminated with 0.2 M sulphuric acid. The absorbance was determined at 405 nm by a microtiter plate reader (Eon model, Inc., Winooski, Vermont, USA), and the concentrations of IgE (ng/mL) in rat serum samples were calculated using the standard curve.
3.1 Structural description of the model
The present study was initiated to perform structure based sequence analysis studies on the lectin from lectin from black turtle beans (Phaseolus vulgaris). The protein sequence was searched using accession #: KF601826 from the NCBI protein database. Primary structure analysis according to Protparam online tool was present at Table 1. The results showed that the allergen protein consisting of 275 amino acids had a molecular weight of 29774.53 Da with the formula: C1346H2095N341O418S1and theoretical isoelectric point (PI) of 4.84. An isoelectric point below 7.0 indicates a negatively charged protein. The instability index is computed to be 19.64 which could classify the protein as stable. The Grand average of hydropathicity (GRAVY) of 0.068 revealed that the allergen protein molecule was hydrophobic. Valine (V), Glycine (G), Alanine (A) and Leucine (L) were observed in rich amounts in the allergen protein. Secondary structure revealed that it had 4.36% alpha helices, 38.18% beta turns, 57.45% random coils (Figure 1a).
Protein three – dimensional structure is very important in understanding the protein molecule interactions, functions and their localization (Idrees & Ashfaq, 2012). Homology modeling is the most common structure prediction method. To perform the homology modeling, the first and basic step is to find a best matching template using similarity searching programs SWISS-MODEL. Templates are selected based on their sequence identity with query sequence. The 3wcs.1.A template was selected for homology modeling, which showed the highest sequence identity (98.43%) with the allergen protein. The three – dimensions structure including tetramer and monomer of the target protein was presented at Fig. 1b and Fig. 1c.
Quality and reliability of structure were checked by several structure assessment methods, including Z-score, ERRAT and Ramachandram plots. Procheck checks the stereochemical quality of a protein structure by analyzing residue-by-residue geometry and overall structure geometry. This tool was used to determine the Ramachandran plot to assure the quality of the model. The result of the Ramachandran plot showed 84.5% of residues in the favorable region (Fig. 1d). The Z-score is indicative of overall model quality and is used to check whether the input structure is within the range of scores typically found in native proteins of comparable size. QMEAN web server was used to find the Z-score of the predicted structure. The Z-score of the protein was 0.30 (Fig. 1e). Reliability of the model was further checked by ERRAT, which analyzes the statistics of non-bonded interactions between different atom types and plots the value of the error function versus position of a 9-residue sliding window, calculated by a comparison with statistics from highly refined structures. Results from ERRAT reveled 91.365 overall model quality (Fig. 1f). The Z-scores, Ramachandran plot and ERRAT results confirmed the quality of the homology model of the allergen protein.
3.2 B-cell epitope prediction
The B cell epitopes of the lectin from black turtle beans were predicted by sequence and structure based methods. The results were showed in Table 2 and 3. A consensus was taken among the commonly predicted epitopes by various prediction tools used in the study. Two antigenic regions were predicted in the allergen protein. The predicted epitopes comprise of regions: 132-144: NYKYDSNAHTVA, 68–80: GRAFYSAPIQIW, 117-127: GSEPKDKGGL, 154-165: WDPKPRHIGID. The result of physicochemical characteristics was presented at Fig. 3. After checking the physicochemical characteristics of the predicted epitopes, it were found that epitope NYKYDSNAHTVA and GSEPKDKGGL obtained higher score in antigenicity, hydrophilicity, average flexibility and surface accessibility. Furthermore, observing its position locating in 3D structure, only NYKYDSNAHTVA was found locating the surface of the allergen protein, which indicated the epitope as the most probable antigens. B-cells epitopes were shown in yellow color in the 3D structure of the allergen protein in Figure 3a.
3.3 T-cell epitope prediction
ProPred-I (47 MHC Class-I alleles) were used to predict T-cell epitopes for the HCV E1 protein. ProPred-I is an online web tool for the prediction of peptides binding to MHC class-I alleles. The allergen protein sequence was uploaded to the Propred-I server while selecting all the alleles, with a high scoring peptide threshold of 4%, and showing the top four epitopes in the tabular form along with proteasome and immunoproteasome filters. All the predicted epitopes were screened for their antigenicity and epitopes that were found to be antigenic in nature were used for further analysis (Table 4). Epitope VKGENAEVL at position 179 was found to obtain the highest antigenicity among all predicted epitopes. T-cells epitopes were shown in yellow color in the 3D structure of the allergen protein in Fig. 3b.
In this study, sequence and structure analysis, homology modeling and epitope study was accomplished on the lectin from black turtle beans (Phaseolus vulgaris). Three dimensional structure is essential to understand the structure function relationship of allergen antibody responses and epitope identification (Govindaraj et al., 2016). In this work, we have spared no effort to predict the 3D structure and promiscuous epitopes among the allergen protein. To accomplish this job, we have employed various types of highly precise bioinformatics web tools, retrieved a large number of data, and an interesting and ideal 3D structure. Through primary structure study (Walker, 2005), we found that it processes the highest number of leucine reaching about 12.4% and its N-terminal residue is leucine. Subsequently, we used a homology modeling approach to predict the 3D structure of the allergen protein. The predicted 3D structure will provide more insight into understanding the structure and function of this protein. Furthermore, the predicted structure can be used for diagnosis, therapeutics and understanding the interactions between protein molecules. The allergen protein was molecularly characterized using various online servers, and it was observed that it processed eight N-glycosylation sites, respectively locating the position of asparagine (N) 5, 33, 36, 55, 57, 81, 131 and 151. The O-glycosylation sites were not found.
As the present study, we predicted B-cell and T-cell epitopes from the Allergen of lectin from black turtle bean (Phaseolus vulgaris) using various online tools. Only four B-cell epitopes were found to be antigenically effective, and it can be inferred that these epitopes/antigenic determinants are important in raising the desired immune response. Using 3D structure of the allergen protein, eight B-cell epitopic locations were identified. All the predicted B-cell epitopes were checked for their localization in the protein structure, and it was found that the majority of predicted epitopes were in the outside region of the protein.
Experimental approaches for predicting epitopes eliciting both B and T cell immunity are time-consuming, costly, and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments by scanning systematically best candidate peptides. Epitopes prediction and analysis would be beneficial for protein function annotation and designing the structure based drugs.
Calis, J. J. A., Maybeno, M., Greenbaum, J. A., Weiskopf, D., Silva, A. D. D., Sette, A., . . . Peters, B. (2013). Properties of MHC Class I Presented Peptides That Enhance Immunogenicity. PLoS Computational Biology,9,10(2013-10-24), 9(10), e1003266.
D’Amato, G., Liccardi, G., & D’Amato, M. (2000). Environmental risk factors (outdoor air pollution and climatic changes) and increased trend of respiratory allergy. Journal of investigational allergology & clinical immunology : official organ of the International Association of Asthmology (INTERASMA) and Sociedad Latinoamericana de Alergia e Inmunología, 10(3), 123.
Dan, X., Liu, W., & Ng, T. B. (2015). Development and Applications of Lectins as Biological Tools in Biomedical Research. Medicinal Research Reviews, 36(2), 221-247.
Frazier, A., Schulten, V., Hinz, D., Oseroff, C., Sidney, J., Peters, B., & Sette, A. (2014). Allergy-associated T cell epitope repertoires are surprisingly diverse and include non-IgE reactive antigens. World Allergy Organization Journal, 7(1), 26.
Furmonaviciene, R., Sutton, B. J., Glaser, F., Laughton, C. A., Jones, N., Sewell, H. F., & Shakib, F. (2005). An attempt to define allergen-specific molecular surface features: a bioinformatic approach. Bioinformatics, 21(23), 4201-4204.
Govindaraj, D., Sharma, S., Gaur, S. N., Lavasa, S., Prasad, N., & Arora, N. (2016). Immunogenic peptides: B & T Cell Epitopes of Per a 10 Allergen of Periplaneta americana. Molecular Immunology, 80, 24-32.
He, S., Simpson, B. K., Ngadi, M. O., & Ma, Y. (2015). In vitro studies of the digestibility of lectin from black turtle bean (Phaseolus vulgaris). Food Chemistry, 173(173), 397.
He, S., Simpson, B. K., Sun, H., Ngadi, M. O., Ma, Y., & Huang, T. (2015). Phaseolus vulgaris Lectins: A Systematic Review of Characteristics and Health Implications. Crit Rev Food Sci Nutr.
Idrees, S., & Ashfaq, U. A. (2012). A brief review on dengue molecular virology, diagnosis, treatment and prevalence in Pakistan. Genetic Vaccines & Therapy, 10(1), 1-10.
Ménard, S., Cerf-Bensussan, N., & Heyman, M. (2010). Multiple facets of intestinal permeability and epithelial handling of dietary antigens. Mucosal Immunology, 3(3), 247-259.
Miyake, K., Tanaka, T., & Mcneil, P. L. (2007). Lectin-based food poisoning: a new mechanism of protein toxicity. Plos One, 2(8), e687.
Ogawa, H., & Date, K. (2014). The “White Kidney Bean Incident” in Japan: Springer New York.
Pom, & Xe, A. (2010). Relevant B Cell Epitopes in Allergic Disease. International Archives of Allergy & Immunology, 152(1), 1-11.
Vasconcelos, I. M., & Oliveira, J. T. A. (2004). Antinutritional properties of plant lectins. Toxicon Official Journal of the International Society on Toxinology, 44(4), 385.
Walker, J. M. (2005). The Proteomics Protocols Handbook: Humana Press.
Weis, W. I., & Drickamer, K. (1996). Structural Basis of Lectin-Carbohydrate Recognition. Annual Review of Biochemistry, 65(65), 441-473.
Wilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J. C., Williams, K. L., Appel, R. D., & Hochstrasser, D. F. (1999). Protein Identification and Analysis Tools in the ExPASy Server. Methods in Molecular Biology, 112(112), 531.
Yang, X., & Yu, X. (2009). An introduction to epitope prediction methods and software. Reviews in Medical Virology, 19(2), 77–96.
Fig.1 a. Predicted secondary structure of the lectin from black turtle bean. b. Predicted the tetramer of the lectin from black turtle bean by SWISS-MODEL. c. Predicted the monomer of the lectin from black turtle bean by Phyre2. d. Ramachandran plot showing residues in the most favorable region and disallowed regions. e. Z-score showing the quality of the three dimensions structure.
Fig. 2 Predicted physicochemical characteristics of the allergen protein. a. The antigenicity analyses for amino acid position, b. The hydrophily and hydrophobicity analyses for amino acid position, c. The surface accessibility analyses for amino acid position, d. The average flexibility analyses for amino acid position.
Fig. 3 Predicted B-cell epitopic regions of the allergen protein 3D structure. a. B-cell epitopic regions were shown in yellow color. b. T-cell epitopic regions were shown in yellow color.
|Number of amino acids||275|
|Total number of negatively charged residues (Asp+Glu)||26|
|Total number of positively charged residues (Arg+Lys)||18|
|Total number of atoms||4201|
|Ext. coeffitient (M-1cm-1)||36440|
|The instability index||19.64|
|Grand average of hydropathicity (GRAVY)||0.068|
Table 1 The result of primary structure analysis according to Protparam online tool (http://web.expasy.org/protparam/)
Table 2 B-cell epitope prediction for the lectin from black turtle bean based ABCPred web servers (http://crdd.osdd.net/raghava//abcpred/).
Table 3 B-cell epitope prediction for the lectin from black turtle bean based BCPREDS server (http://ailab.ist.psu.edu/bcpred/index.html).
|103||SGPADGLAF||HLA-A1/ HLA-B*4403/ HLA-B*5201/ MHC-Dd/ MHC-Ld||0.10277|
|256||SDGTTSEAL||HLA-B*3701/ HLA-B60/HLA-Cw*0702/MHC-Dd/ MHC-Kk||0.03949|
Table 4 T-cell epitope prediction for the lectin from black turtle bean based ProPred-I online web server (http://crdd.osdd.net/raghava/propred1/).
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