2021
DOI: 10.1016/j.fct.2020.111888
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Evaluating potential risks of food allergy of novel food sources based on comparison of proteins predicted from genomes and compared to www.AllergenOnline.org

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Cited by 52 publications
(31 citation statements)
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“…Moreover, enolase (G3GDU2_PENVA), with 117 amino acid residues, has the same sequence as residues 178–294 of phosphopyruvate hydratase (O96656_PENVA). Homologous amino acid sequences are one of the important factors in predicting allergic activity (Abdelmoteleb et al., 2021). Therefore, phosphopyruvate hydratase was predicted to be a potential allergen, and it may have the same domain for allergic reactions with enolase.…”
Section: Resultsmentioning
confidence: 99%
“…Moreover, enolase (G3GDU2_PENVA), with 117 amino acid residues, has the same sequence as residues 178–294 of phosphopyruvate hydratase (O96656_PENVA). Homologous amino acid sequences are one of the important factors in predicting allergic activity (Abdelmoteleb et al., 2021). Therefore, phosphopyruvate hydratase was predicted to be a potential allergen, and it may have the same domain for allergic reactions with enolase.…”
Section: Resultsmentioning
confidence: 99%
“…The highly conservative current approach appears to lead to a high number of false positives (Ladics et al, 2007 ; Abdelmoteleb et al, 2021 ; Herman et al, 2021 ). A full FASTA approach with appropriate match criteria has claimed to be as sensitive as the 35% identity over an 80‐aa sliding window approach, while the specificity is significantly higher (Ladics et al, 2007 ; Silvanovich et al, 2009 ; Abdelmoteleb et al, 2021 ). Conversely, there are studies reporting experimental IgE cross‐reactivity between proteins despite a low sequence identity (i.e.…”
Section: Assessmentmentioning
confidence: 99%
“…These also include alternative or complementary approaches beyond sequence alignment principles as defined by Codex. Some selected examples of alternative in silico approaches are (i) increasing the match criteria above 35% identity and decreasing the E ‐score below 1e ‐7 or smaller (Abdelmoteleb et al, 2021 ); (ii) numerical descriptors representing the physicochemical properties of the amino acid in protein sequence and machine learning approach for classification of allergens (Dimitrov et al, 2013 , 2014a ); (iii) similarity of their 3D protein structure as well as their amino acid sequence (Maurer‐Stroh et al, 2019 ); (iv) similarity search to a data set of allergenic and non‐allergenic proteins represented as binary fingerprints (Dimitrov et al, 2014b ); (v) machine learning approaches based on mapping of IgE epitope, motif search and/or other selected variables (Westerhout et al, 2019 ; Sharma et al, 2021 ); (vi) as well as novel approaches considering human leucocyte antigens (HLA) binders from known allergens for the in silico assessment of the sensitisation potential of innovative/novel proteins (Dimitrov & Atanasova, 2020 ).…”
Section: Assessmentmentioning
confidence: 99%
“…Novel food and novel food sources have encouraged interest of the scientific community, producers and consumers to the development of new and innovative products and technologies, as well as research into possible health benefits and / or risks (Knežević et. al, 2012;Caporgno and Mathys, 2018;Majid et al 2018;Mazzucchelli et al, 2018;Cappelli et al, 2020;Abdelmoteleb et al, 2021).…”
Section: Introductionmentioning
confidence: 99%