Predictive in silico binding algorithms reveal HLA specificities and autoallergen peptides associated with atopic dermat
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ORIGINAL PAPER
Predictive in silico binding algorithms reveal HLA specificities and autoallergen peptides associated with atopic dermatitis Jan J. Gong1 · David J. Margolis1,2,3 · Dimitrios S. Monos1,4,5 Received: 14 February 2020 / Accepted: 26 February 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Atopic dermatitis · HLA alleles · Autoreactivity
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00403-020-02059-0) contains supplementary material, which is available to authorized users. * David J. Margolis [email protected] Jan J. Gong [email protected] Dimitrios S. Monos [email protected] 1
Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
2
Department of Dermatology, University of Pennsylvania, Philadelphia, PA, USA
3
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
4
Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
5
Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
Abbreviations AD Atopic dermatitis IEDB Immune Epitope Database Analysis Resource HA Hemagglutinin
Introduction Currently, existing in silico T-cell epitope discovery algorithms, given a full-length protein and an HLA allele, are able to predict likely peptide epitopes and their binding affinities to HLA molecules [1–5]. Immune Epitope Database Analysis Resource (IEDB) and NetMHCIIpan 3.1 are epitope predictio
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