Bioinformatics approaches to classifying allergens and predicting cross-reactivity.

Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Research Abstract Details 

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Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Abstract Text:

Catherine H Schein,Ovidiu Ivanciuc,Werner Braun,

Allergenic proteins from very different environmental sources have similar sequences and structures. This fact may account for multiple allergen syndromes, whereby a myriad of diverse plants and foods may induce a similar IgE-based reaction in certain patients. Identifying the common triggering protein in these sources, in silico, can aid designing individualized therapy for allergen sufferers. This article provides an overview of databases on allergenic proteins, and ways to identify common proteins that may be the cause of multiple allergy syndromes. The major emphasis is on the relational Structural Database of Allergenic Proteins (SDAP []), which includes cross-referenced data on the sequence, structure, and IgE epitopes of over 800 allergenic proteins, coupled with specially developed bioinformatics tools to group all allergens and identify discrete areas that may account for cross-reactivity. SDAP is freely available on the Web to clinicians and patients.

Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Publishing Authors By Initials

CH Schein,O Ivanciuc,W Braun,

For similar proteins research abstracts see: proteins research

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Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Journal Published:

PUBLICATION TYPE: Review

Journal: Immunology and allergy clinics of North America

VOLUME: 27

Page Numbers: 1-27

Journal Abbreviation:

ISSN: 0889-8561

DAY: 3

MONTH: Feb

YEAR: 2007

Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Information

Number of References: 131

LANGUAGE: eng

NlmUniqueID: 8805635

Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Keywords Mesh Terms:

KEYWORDS: Proteins

MESH TERMS: immunology

Chemical & Substance for Abstract: Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for Bioinformatics approaches to classifying allergens and predicting cross-reactivity.

AFFILIATION: Sealy Center for Structural Biology and Molecular Biophysics, Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-0857, USA.

Country: United States

AGENCY: United States NIAID

GRANT: R01 AI064913-02

ACRONYM: AI

MEDLINETA: Immunol Allergy Clin North Am

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