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Archives of Dermatological Research

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09.03.2020 | Original Paper

Predictive in silico binding algorithms reveal HLA specificities and autoallergen peptides associated with atopic dermatitis

verfasst von: Jan J. Gong, David J. Margolis, Dimitrios S. Monos

Erschienen in: Archives of Dermatological Research | Ausgabe 9/2020

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Abstract

Atopic dermatitis (AD) is a skin disease that results from a combination of skin barrier dysfunction and immune dysregulation. The immune dysregulation is often associated with IgE sensitivity. There is also evidence that autoallergens Hom s 1, 2, 3, and 4 play a role in AD; it is possible that patients with specific HLA subtypes are predisposed to autoreactivity due to increased presentation of autoallergen peptides. The goal of our study was to use in silico epitope prediction platforms as an approach to identify HLA subtypes that may preferentially bind autoallergen peptides and are thus candidates for further study. Considering the previously described association of DRB1 alleles with AD and progression of disease, emphasis was placed on DRB1. Certain DRB1 alleles (08:04, 11:01, and 11:04) were identified by both algorithms to bind a significant percent of the generated autoallergen peptides. Conversely, autoallergen core peptide sequences FRQLSHRFH and IRAKLRLQA (Hom s 1), IRKSKNILF (Hom s 2), FKWVPVTDS and MAAIEKVRK (Hom s 3), and FRYFATLKV (Hom s 4) were predicted to bind many DRB1 alleles and, thus, may play a role in the pathogenesis of AD. Our findings provide candidate DRB1 alleles and autoallergen epitopes that will guide future studies exploring the relationship between DRB1 subtype and autoreactivity in AD. A similar approach can be used for any antigen that has been associated with an IgE response and AD.

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Titel: Predictive in silico binding algorithms reveal HLA specificities and autoallergen peptides associated with atopic dermatitis
verfasst von: Jan J. Gong
David J. Margolis
Dimitrios S. Monos
Publikationsdatum: 09.03.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Archives of Dermatological Research / Ausgabe 9/2020
Print ISSN: 0340-3696
Elektronische ISSN: 1432-069X
DOI: https://doi.org/10.1007/s00403-020-02059-0

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