Saravanan V, Gautham N
Center for Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, Tamil Nadu, 600025 India.
Mol Biol (Mosk). 2018 Mar-Apr;52(2):333-343. doi: 10.7868/S0026898418020180.
Allergy is a common health problem worldwide, especially food allergy. Since B cell epitopes that are recognized by the IgE antibodies act as antigenic determinants for allergy, they play a vital role in diagnostics. Hence, knowledge of an IgE binding epitope in a protein is of particular interest for identifying aller-genic proteins. Though IgE epitopes maybe conformational or linear, identification of the later is useful especially in food allergens that undergo processing or digestion. Very few computational tools are available for the prediction of linear IgE epitopes. Here we report a prediction system that predicts the exact linear IgE epitope. Since our earlier study on linear B cell epitope prediction demonstrated the effectiveness of using an exact epitope dataset (in contrast to epitope containing region datasets), the dataset in this study uses only experimentally verified exact IgE, IgG, IgM and IgA epitopes. Models for Support Vector Machine (SVM) and Random Forest (RF) were constructed adopting Dipeptide Deviation from the Expected mean (DDE) feature vector. Extensive validation procedures including five-fold cross validation and two different independent dataset tests have been performed to validate the proposed method, which achieved a balanced accuracy ranging from 74 to 78% with area under receiver operator curve greater than 0.8. Performance of the proposed method was observed to be better (accuracy difference of 16-28%) in comparison to the existing available method. The proposed method is developed as a standalone tool that could be used for predicting IgE epitopes as well as to be incorporated into any allergen prediction toolhttps://github.com/brsaran/BCIgePred.
过敏是全球常见的健康问题，尤其是食物过敏。由于被IgE抗体识别的B细胞表位作为过敏的抗原决定簇，它们在诊断中起着至关重要的作用。因此，了解蛋白质中的IgE结合表位对于鉴定过敏原蛋白尤为重要。尽管IgE表位可能是构象性的或线性的，但鉴定后者尤其在经过加工或消化的食物过敏原中很有用。用于预测线性IgE表位的计算工具非常少。在此，我们报告一种预测系统，该系统可预测确切的线性IgE表位。由于我们早期关于线性B细胞表位预测的研究证明了使用确切表位数据集（与包含表位区域的数据集相比）的有效性，本研究中的数据集仅使用经实验验证的确切IgE、IgG、IgM和IgA表位。采用偏离预期均值的二肽（DDE）特征向量构建支持向量机（SVM）和随机森林（RF）模型。已经进行了广泛的验证程序，包括五折交叉验证和两个不同的独立数据集测试，以验证所提出的方法，该方法实现了74%至78%的平衡准确率，受试者工作特征曲线下面积大于0.8。与现有可用方法相比，所提出方法的性能被观察到更好（准确率差异为16 - 28%）。所提出的方法被开发为一个独立工具，可用于预测IgE表位，也可纳入任何过敏原预测工具https://github.com/brsaran/BCIgePred。
