PMID- 36335724
OWN - NLM
STAT- MEDLINE
DCOM- 20221207
LR  - 20221207
IS  - 1873-7072 (Electronic)
IS  - 0308-8146 (Linking)
VI  - 405
IP  - Pt A
DP  - 2023 Mar 30
TI  - Qualitative and quantitative prediction of food allergen epitopes based on 
      machine learning combined with in vitro experimental validation.
PG  - 134796
LID - S0308-8146(22)02758-3 [pii]
LID - 10.1016/j.foodchem.2022.134796 [doi]
AB  - 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 beta-LG.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Yu, Xin-Xin
AU  - Yu XX
AD  - Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural 
      University, Harbin 150030, PR China; Department of Food Science, Northeast 
      Agricultural University, Harbin 150030, PR China.
FAU - Liu, Meng-Qi
AU  - Liu MQ
AD  - Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural 
      University, Harbin 150030, PR China; Department of Food Science, Northeast 
      Agricultural University, Harbin 150030, PR China.
FAU - Li, Xiao-Yan
AU  - Li XY
AD  - Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural 
      University, Harbin 150030, PR China; Department of Food Science, Northeast 
      Agricultural University, Harbin 150030, PR China.
FAU - Zhang, Ying-Hua
AU  - Zhang YH
AD  - Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural 
      University, Harbin 150030, PR China; Department of Food Science, Northeast 
      Agricultural University, Harbin 150030, PR China; National Center of Technology 
      Innovation for Dairy, Hohhot 010020, PR China. Electronic address: 
      yinghuazhang@neau.edu.cn.
FAU - Tao, Bing-Jie
AU  - Tao BJ
AD  - Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural 
      University, Harbin 150030, PR China; Department of Food Science, Northeast 
      Agricultural University, Harbin 150030, PR China.
LA  - eng
PT  - Journal Article
DEP - 20221101
PL  - England
TA  - Food Chem
JT  - Food chemistry
JID - 7702639
RN  - 0 (Epitopes)
RN  - 37341-29-0 (Immunoglobulin E)
RN  - 0 (Allergens)
SB  - IM
MH  - Humans
MH  - Epitopes/chemistry
MH  - *Food Hypersensitivity
MH  - Immunoglobulin E/metabolism
MH  - Allergens/chemistry
MH  - Machine Learning
OTO - NOTNLM
OT  - ELISA
OT  - IgE-binding epitopes
OT  - Machine learning
OT  - Prediction
OT  - QSAR
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/11/07 06:00
MHDA- 2023/02/25 06:00
CRDT- 2022/11/06 18:13
PHST- 2021/05/30 00:00 [received]
PHST- 2022/10/25 00:00 [revised]
PHST- 2022/10/26 00:00 [accepted]
PHST- 2022/11/07 06:00 [pubmed]
PHST- 2023/02/25 06:00 [medline]
PHST- 2022/11/06 18:13 [entrez]
AID - S0308-8146(22)02758-3 [pii]
AID - 10.1016/j.foodchem.2022.134796 [doi]
PST - ppublish
SO  - Food Chem. 2023 Mar 30;405(Pt A):134796. doi: 10.1016/j.foodchem.2022.134796. 
      Epub 2022 Nov 1.