Background: A large number of allergens are derived from plant and animal proteins. A major challenge for researchers is to study the possible allergenic properties of proteins. The aim of this study was in silico analysis and comparison of several physiochemical and structural features of plant- and animal-derived allergen proteins, as well as classifying these proteins based on Chou’s pseudo-amino acid composition (PseAAC) concept combined with bioinformatics algorithms. Methods: The physiochemical properties and secondary structure of plant and animal allergens were studied. The classification of the sequences was done using the PseAAC concept incorporated with the deep learning algorithm. Conserved motifs of plant and animal proteins were discovered using the MEME tool. B-cell and T-cell epitopes of the proteins were predicted in conserved motifs. Allergenicity and amino acid composition of epitopes were also analyzed via bioinformatics servers. Results: In comparison of physiochemical features of animal and plant allergens, extinction coefficient was different significantly. Secondary structure prediction showed more random coiled structure in plant allergen proteins compared with animal proteins. Classification of proteins based on PseAAC achieved 88.24% accuracy. The amino acid composition study of predicted B- and T-cell epitopes revealed more aliphatic index in plant-derived epitopes. Conclusions: The results indicated that bioinformatics-based studies could be useful in comparing plant and animal allergens.
Int Arch Allergy Immunol

中文翻译：

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使用几种计算工具和Chou的PseAAC概念比较动植物之间的过敏原蛋白。

背景：大量的过敏原来源于植物和动物蛋白。研究人员面临的主要挑战是研究蛋白质可能的致敏特性。这项研究的目的是计算机分析和比较植物和动物来源的过敏原​​蛋白的几种物理化学和结构特征，以及基于周的伪氨基酸组成（PseAAC）概念和生物信息学算法对这些蛋白进行分类。方法：研究了动植物过敏原的理化性质和二级结构。使用结合了深度学习算法的PseAAC概念对序列进行分类。使用MEME工具发现了动植物蛋白质的保守基序。蛋白质的B细胞和T细胞表位在保守基序中被预测。还通过生物信息学服务器分析了表位的致敏性和氨基酸组成。结果：在比较动植物变应原的理化特性时，消光系数差异显着。二级结构预测显示，与动物蛋白相比，植物过敏原蛋白中的卷曲结构更为随机。基于PseAAC的蛋白质分类达到88.24％的准确性。预测的B细胞和T细胞表位的氨基酸组成研究表明，植物来源表位中的脂肪族指数更高。结论：结果表明，基于生物信息学的研究可用于比较植物和动物的过敏原。
Int Arch过敏免疫