Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st,, Sofia, Bulgaria.
BMC Bioinformatics. 2013;14 Suppl 6(Suppl 6):S4. doi: 10.1186/1471-2105-14-S6-S4. Epub 2013 Apr 17.
Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.
A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.
AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.
过敏是一种对通常无害物质（如灰尘、花粉、食物或药物）的超敏反应形式。过敏原是引发 IgE 抗体反应的小抗原。有两种基于生物信息学的过敏原预测方法。第一种方法遵循粮农组织/世界卫生组织食品法典委员会的准则，并搜索序列相似性。第二种方法基于识别保守的变应原性相关线性基序。这两种方法都假设变应原性是一种线性编码的特性。在本研究中，我们应用了 ACC 预处理程序对已知过敏原集进行处理，基于氨基酸序列的主要化学性质开发了用于过敏原识别的不依赖于比对的模型。
从多个数据库中收集了一组 684 种食物、1156 种吸入性和 555 种毒素过敏原。从同一物种中选择了一组非过敏原作为过敏原集的镜像。蛋白质序列中的氨基酸用三个 z 描述符（z1、z2 和 z3）和自协方差（ACC）变换来描述，转化为统一的向量。每个蛋白质都表示为 45 个变量的向量。应用了五种分类的机器学习方法来推导过敏原预测模型。这些方法是：偏最小二乘判别分析（DA-PLS）、逻辑回归（LR）、决策树（DT）、朴素贝叶斯（NB）和 k 最近邻（kNN）。表现最好的模型是通过 kNN 得出的，k = 3。该模型经过优化、交叉验证并实现到一个名为 AllerTOP 的服务器中，该服务器可在 http://www.pharmfac.net/allertop 上免费访问。AllerTOP 还预测最有可能的暴露途径。与其他过敏原预测服务器相比，AllerTOP 的灵敏度达到 94%，表现更为出色。
AllerTOP 是第一个基于蛋白质主要物理化学性质的无比对服务器，用于计算预测过敏原。重要的是，除了变应原性外，AllerTOP 还能够预测过敏原暴露的途径：食物、吸入物或毒素。
