Computational identification of Brassica napus pollen specific protein Bnm1 as an allergen
| Main Author: | Md. Rezaul Islama,* , Ahsan Habib Polashb , M Sadman Sakiba , Chinmoy Sahac , Atiqur Rahman |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2013
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/1405329 |
Daftar Isi:
- International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.2, June 2013 DOI: 10.5121/ijbb.2013.3205 45 Computational identification of Brassica napus pollen specific protein Bnm1 as an allergen Md. Rezaul Islama,* , Ahsan Habib Polashb , M Sadman Sakiba , Chinmoy Sahac , Atiqur Rahmana aDepartment of Biochemistry and Molecular Biology, University of Dhaka, Dhaka-1000, Bangladesh bMolecular Biology Laboratory, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka-1000, Bangladesh c Postgraduate School of Molecular Medicine, Erasmus MC, Rotterdam, The Netherlands. E-mail address: rezaul.nayeem@gmail.com Abstract Bnm1 is a pollen specific protein from Brassica napus (oilseed rape) and it is specifically expressed in the bi-cellular and tri-cellular stages of pollen development. Since the incidence of pollinosis due to oilseed rape (Brassica napus) is increasing day by day, keeping pace with its high cultivation rate, the search for its allergens is a demand of time to develop effective immune therapy. In the present study, different computational tools were adopted to predict the potential of Bnm1 as a candidate allergen. Physicochemical properties of Bnm1 showed its molecular weight (~20kD) and theoretical pI (5.27) along with other properties to be fallen between the ranges essential for a protein to be an allergen. Keeping in mind the capability of allergen to induce both humoral and cell mediated immune response, we checked both the potential B cell and T cell epitope candidates of Bnm1 using different immune-informatics tools housed at IEDB analysis resource. For B cell epitope prediction, potential antigenic sites on the protein surface were predicted by both propensity scale and machine learning method followed by their mapping on Bnm1 3D structure predicted from homology modeling. In case of T cell epitope prediction, interaction of the core sequence with seven abundant MHC-II alleles (DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1301 and DRB1*1501) within an IC50 range (IC50(Brassica napus) Pollen Profilin as a Cross-Reactive Allergen. International Archives of Allergy and Immunology, 2003. 132(2): p. 116- 123. 20. de Weerd, N.A., P.L. Bhalla, and M.B. Singh, Aeroallergens and pollinosis: Molecular and immunological characteristics of cloned pollen allergens. Aerobiologia, 2002. 18(2): p. 87-106. 21. Md. Rezaul Islam, M.S.S., Aubhishek Zaman, A computational assay to design an epitope-based peptide vaccine against chikungunya virus. Future Virology, 2012. 7(10): p. 1029-1042. 22. Tchernitchko, D., M. Goossens, and H. Wajcman, In silico prediction of the deleterious effect of a mutation: proceed with caution in clinical genetics. Clinical chemistry, 2004. 50(11): p. 1974-1978. 23. Treacy, B.K., et al., Bnm1, a Brassica pollen-specific gene. Plant molecular biology, 1997. 34(4): p. 603-611. 24. Gasteiger, E., et al., Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook, 2005: p. 571-607. 25. Kolaskar, A. and P.C. Tongaonkar, A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS letters, 1990. 276(1): p. 172-174. 26. Parker, J., D. Guo, and R. Hodges, 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(19): p. 5425-5432. 27. Larsen, J.E.P., O. Lund, and M. Nielsen, Improved method for predicting linear B-cell epitopes. Immunome research, 2006. 2(1): p. 2. 28. Källberg, M., et al., Template-based protein structure modeling using the RaptorX web server. Nature Protocols, 2012. 7(8): p. 1511-1522. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.2, June 2013 55 29. Johansson, M.U., et al., Defining and searching for structural motifs using DeepView/SwissPdbViewer. BMC bioinformatics, 2012. 13(1): p. 173. 30. Laskowski, R.A., et al., PROCHECK: a program to check the stereochemical quality of protein structures. Journal of applied crystallography, 1993. 26(2): p. 283-291. 31. Gowthaman, U. and J.N. Agrewala, In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion. Journal of proteome research, 2007. 7(01): p. 154-163. 32. Nielsen, M., et al., Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS computational biology, 2008. 4(7): p. e1000107. 33. De Groot, A.S., et al., Activation of natural regulatory T cells by IgG Fc–derived peptide “Tregitopes”. Blood, 2008. 112(8): p. 3303-3311. 34. Singh, S., et al., Physical Properties of Intact Proteins May Predict Allergenicity or Lack Thereof. PloS one, 2009. 4(7): p. e6273. 35. Bachmair, A., In vivo half-life of a protein is a function of its. Science, 1986. 3018930(179): p. 234. 36. Varshavsky, A., The N‐end rule pathway of protein degradation. Genes to Cells, 1997. 2(1): p. 13- 28. 37. Hopp, T.P. and K.R. Woods, Prediction of protein antigenic determinants from amino acid sequences. Proceedings of the National Academy of Sciences, 1981. 78(6): p. 3824-3828. 38. Blythe, M.J. and D.R. Flower, Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Science, 2009. 14(1): p. 246-248. 39. Chen, J., et al., Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 2007. 33(3): p. 423-428. 40. Söllner, J. and B. Mayer, Machine learning approaches for prediction of linear B‐cell epitopes on proteins. Journal of Molecular Recognition, 2006. 19(3): p. 200-208. 41. DeLano, W.L., The PyMOL molecular graphics system. 2002. 42. Motta, A., et al., Phleum pratense pollen starch granules induce humoral and cell‐mediated immune responses in a rat model of allergy. Clinical & Experimental Allergy, 2004. 34(2): p. 310-314. 43. Lee, S.C., et al., Regulation of pulmonary T cell responses to inhaled antigen: role in Th1-and Th2- mediated inflammation. The Journal of Immunology, 1999. 162(11): p. 6867-6879. 44. Boulet, L.P., Allergen-derived T cell peptides and late asthmatic responses. American journal of respiratory and critical care medicine, 2004. 169(1): p. 2-3. 45. Janeway CA Jr, T.P., Walport M, et al., Immunobiology: The Immune System in Health and Disease. . 2001. Supplementary Files: Supplementary Table 1 Predicted T cell candidate epitopes having IC50 < 25.0 score Allele name Start Position End position Core sequence Peptide sequence IC50 score HLA-DRB1*01:01 19 27 LLLVPASAS TLQLLLVPASASPHM 4.8 HLA-DRB1*01:01 19 27 LLLVPASAS LQLLLVPASASPHMK 5.0 HLA-DRB1*01:01 17 25 LQLLLVPAS ITLQLLLVPASASPH 5.4 HLA-DRB1*01:01 19 27 LLLVPASAS QLLLVPASASPHMKY 6.4 HLA-DRB1*01:01 17 25 LQLLLVPAS AITLQLLLVPASASP 6.5 HLA-DRB1*01:01 17 25 LQLLLVPAS AAITLQLLLVPASAS 7.4 HLA-DRB1*03:01 103 111 VVADLKSAN YLAVVADLKSANLK L 7.7 International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.2, June 2013 56 HLA-DRB1*01:01 160 168 LLDLAASAA MEKLLDLAASAADA V 9.2 HLA-DRB1*01:01 6 14 VLSTFAAAA TFSVLSTFAAAAITL 10.0 HLA-DRB1*03:01 103 111 VVADLKSAN AYLAVVADLKSANL K 10.0 HLA-DRB1*01:01 4 12 FSVLSTFAA MATFSVLSTFAAAAI 10.3 HLA-DRB1*01:01 6 14 VLSTFAAAA ATFSVLSTFAAAAIT 10.9 HLA-DRB1*01:01 6 14 VLSTFAAAA FSVLSTFAAAAITLQ 12.4 HLA-DRB1*01:01 6 14 VLSTFAAAA SVLSTFAAAAITLQL 12.5 HLA-DRB1*01:01 19 27 LLLVPASAS LLLVPASASPHMKYI 12.8 HLA-DRB1*03:01 103 111 VVADLKSAN LAVVADLKSANLKL K 13.0 HLA-DRB1*01:01 10 18 FAAAAITLQ VLSTFAAAAITLQLL 13.2 HLA-DRB1*01:01 160 168 LLDLAASAA QMEKLLDLAASAAD A 13.3 HLA-DRB1*01:01 160 168 LLDLAASAA EKLLDLAASAADAV D 13.3 HLA-DRB1*01:01 10 18 FAAAAITLQ LSTFAAAAITLQLLL 13.4 HLA-DRB1*01:01 71 79 LTIAHAEKT VMALTIAHAEKTAAF 13.7 Supplementary Table 1 (Continued) Predicted T cell candidate epitopes having IC50 < 25.0 score Allele name Start Position End position Core sequence Peptide sequence IC50 score HLA-DRB1*01:01 104 112 VADLKSANL LAVVADLKSANLKLK 14.2 HLA-DRB1*01:01 17 25 LQLLLVPAS AAAITLQLLLVPASA 14.3 HLA-DRB1*01:01 100 108 YLAVVADLK HKAYLAVVADLKSAN 14.7 HLA-DRB1*01:01 104 112 VADLKSANL YLAVVADLKSANLKL 14.7 HLA-DRB1*01:01 100 108 YLAVVADLK YHKAYLAVVADLKSA 16.9 HLA-DRB1*01:01 100 108 YLAVVADLK KAYLAVVADLKSANL 17.0 International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.2, June 2013 57 HLA-DRB1*01:01 160 168 LLDLAASAA KLLDLAASAADAVDD 18.3 HLA-DRB1*03:01 103 111 VVADLKSAN KAYLAVVADLKSANL 19.8 HLA-DRB1*01:01 71 79 LTIAHAEKT EVMALTIAHAEKTAA 20.5 HLA-DRB1*01:01 100 108 YLAVVADLK AYLAVVADLKSANLK 20.7 HLA-DRB1*01:01 10 18 FAAAAITLQ STFAAAAITLQLLLV 21.8 HLA-DRB1*01:01 71 79 LTIAHAEKT MALTIAHAEKTAAFV 23.9