Literature DB >> 18950868

The property distance index PD predicts peptides that cross-react with IgE antibodies.

Ovidiu Ivanciuc1, Terumi Midoro-Horiuti, Catherine H Schein, Liping Xie, Gilbert R Hillman, Randall M Goldblum, Werner Braun.   

Abstract

Similarities in the sequence and structure of allergens can explain clinically observed cross-reactivities. Distinguishing sequences that bind IgE in patient sera can be used to identify potentially allergenic protein sequences and aid in the design of hypo-allergenic proteins. The property distance index PD, incorporated in our Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/), may identify potentially cross-reactive segments of proteins, based on their similarity to known IgE epitopes. We sought to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to three linear IgE epitopes of Jun a 1, the dominant allergen from mountain cedar pollen. For each of the three epitopes, 60 peptides were designed with increasing PD values (decreasing physicochemical similarity) to the starting sequence. The peptides synthesized on a derivatized cellulose membrane were probed with sera from patients who were allergic to Jun a 1, and the experimental data were interpreted with a PD classification method. Peptides with low PD values relative to a given epitope were more likely to bind IgE from the sera than were those with PD values larger than 6. Control sequences, with PD values between 18 and 20 to all the three epitopes, did not bind patient IgE, thus validating our procedure for identifying negative control peptides. The PD index is a statistically validated method to detect discrete regions of proteins that have a high probability of cross-reacting with IgE from allergic patients.

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Year:  2008        PMID: 18950868      PMCID: PMC2651743          DOI: 10.1016/j.molimm.2008.09.004

Source DB:  PubMed          Journal:  Mol Immunol        ISSN: 0161-5890            Impact factor:   4.407


  53 in total

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Journal:  J Biol Chem       Date:  2004-11-10       Impact factor: 5.157

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Review 6.  Bioinformatics approaches to classifying allergens and predicting cross-reactivity.

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  23 in total

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Journal:  Bioinformatics       Date:  2017-04-01       Impact factor: 6.937

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Review 7.  Structural analysis of linear and conformational epitopes of allergens.

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Review 8.  Safety assessment of biotechnology products for potential risk of food allergy: implications of new research.

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9.  Serological, genomic and structural analyses of the major mite allergen Der p 23.

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