Qualitative and quantitative prediction of food allergen epitopes based on machine learning combined with in vitro experimental validation. (30th March 2023)

• Record Type: Journal Article
• Authors: Yu, Xin-Xin; Liu, Meng-Qi; Li, Xiao-Yan; Zhang, Ying-Hua; Tao, Bing-Jie
• Is Part Of: Food chemistry. Volume 405:Part A(2023)
• Publication Date: 2023-03-30
• Subjects: IgE-binding epitopes -- Prediction -- Machine learning -- QSAR -- ELISA; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.134796
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Identification of epitopes using bioinformatic approach. The quantitative model of IgE binding ability was realized based on the database. A novel epitope of β-lactoglobulin (116–130) was determined. The experimental results are consistent with the model prediction. Abstract: An allergen epitope is a part of molecules that can specifically bind to immunoglobulin E (IgE), causing an allergic reactions. To predict protein epitopes and their binding ability to IgE, quantitative structure–activity relationship (QSAR) models were established using four algorithms combined with the selected chemical descriptors. The model predicted the binding capabilities of the epitopes to IgE with the R 2 and root mean squared error (RMSE) as 0.7494 and 0.2375, respectively. The model's performance was validated using an enzyme-linked immunosorbent assay (ELISA). The results showed that the established QSAR model could efficiently and accurately predict the allergic reaction of food protein epitopes. The prediction results of the model and the experimental results were consistent, with a Pearson correlation coefficient of 0.8956. The results from both the QSAR model and in vitro experiments indicated that amino acid sequence 116–130 was a novel IgE-binding epitope of β-LG.