Food Engineering & Technology Department, Institute of Chemical Technology, Mumbai, India(1).
Comput Biol Chem. 2013 Dec;47:149-55. doi: 10.1016/j.compbiolchem.2013.08.003. Epub 2013 Sep 17.
Allergy has become a key cause of morbidity worldwide. Although many legumes (plants in the Fabaceae family) are healthy foods, they may have a number of allergenic proteins. A number of allergens have been identified and characterized in Fabaceae family, such as soybean and peanut, on the basis of biochemical and molecular biological approaches. However, our understanding of the allergens from chickpea (Cicer arietinum L.), belonging to this family, is very limited.
In this study, we aimed to identify putative and cross-reactive allergens from Chickpea (C. arietinum) by means of in silico analysis of the chickpea protein sequences and allergens sequences from Fabaceae family.
We retrieved known allergen sequences in Fabaceae family from the IUIS Allergen Nomenclature Database. We performed a protein BLAST (BLASTp) on these sequences to retrieve the similar sequences from chickpea. We further analyzed the retrieved chickpea sequences using a combination of in silico tools, to assess them for their allergenicity potential. Following this, we built structure models using FUGUE: Sequence-structure homology; these models generated by the recognition tool were viewed in Swiss-PDB viewer.
Through this in silico approach, we identified seven novel putative allergens from chickpea proteome sequences on the basis of similarity of sequence, structure and physicochemical properties with the known reported legume allergens. Four out of seven putative allergens may also show cross reactivity with reported allergens since potential allergens had common sequence and structural features with the reported allergens.
The in silico proteomic identification of the allergen proteins in chickpea provides a basis for future research on developing hypoallergenic foods containing chickpea. Such bioinformatics approaches, combined with experimental methodology, will help delineate an efficient and comprehensive approach to assess allergenicity and pave the way for a better understanding of the biological and medical basis of the same.
过敏已成为全球发病率的主要原因。尽管许多豆类（豆科植物）是健康食品，但它们可能含有多种过敏原蛋白。基于生化和分子生物学方法，已在豆科植物中鉴定和描述了许多过敏原，例如大豆和花生。然而，我们对属于该家族的鹰嘴豆（Cicer arietinum L.）过敏原的了解非常有限。
本研究旨在通过对鹰嘴豆蛋白序列和豆科家族过敏原序列的计算机分析，鉴定出鹰嘴豆（C. arietinum）的潜在和交叉反应性过敏原。
我们从 IUIS 过敏原命名数据库中检索了豆科家族中已知的过敏原序列。我们对这些序列进行蛋白质 BLAST（BLASTp），以从鹰嘴豆中检索相似序列。我们进一步使用组合的计算机工具分析检索到的鹰嘴豆序列，以评估它们的致敏潜能。之后，我们使用 FUGUE：序列结构同源性构建结构模型；这些由识别工具生成的模型在 Swiss-PDB viewer 中查看。
通过这种计算机方法，我们根据与已知报道的豆科过敏原在序列、结构和理化性质上的相似性，从鹰嘴豆蛋白质组序列中鉴定出七种新的潜在过敏原。在七种潜在过敏原中，有四种可能也与报道的过敏原发生交叉反应，因为潜在过敏原与报道的过敏原具有共同的序列和结构特征。
鹰嘴豆过敏原蛋白的计算机蛋白质组学鉴定为未来开发含有鹰嘴豆的低过敏性食物的研究提供了基础。这种生物信息学方法结合实验方法，将有助于确定一种高效、全面的评估致敏性的方法，并为更好地理解其生物学和医学基础铺平道路。
