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Bioinformatics
Article . 2009 . Peer-reviewed
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Bioinformatics
Article . 2010
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High-throughput minor histocompatibility antigen prediction

Authors: David S. DeLuca; Britta Eiz-Vesper; Nektarios Ladas; Barbara Anna-Maria Khattab; Rainer Blasczyk;

High-throughput minor histocompatibility antigen prediction

Abstract

Abstract Motivation: Minor histocompatibility antigens (mHags) are a diverse collection of MHC-bound peptides that have immunological implications in the context of allogeneic transplantation because of their differential presence in donor and host, and thus play a critical role in the induction of the detrimental graft-versus-host disease (GvHD) or in the development of the beneficial graft-versus-leukemia (GvL) effect. Therefore, the search for mHags has implications not only for preventing GvHD, but also for therapeutic applications involving leukemia-specific T cells. We have created a web-based system, named PeptideCheck, which aims to augment the experimental discovery of mHags using bioinformatic means. Analyzing peptide elution data to search for mHags and predicting mHags from polymorphism and protein databases are the core features. Results: Comparison with known mHag data reveals that some but not all of the previously known mHags can be reproduced. By applying a system of filtering and ranking, we were able to produce an ordered list of potential mHag candidates in which HA-1, HA-3 and HA-8 occur in the best 0.25%. By combining single nucleotide polymorphism, protein, tissue expression and genotypic frequency data, together with antigen presentation prediction algorithms, we propose a list of the best peptide candidates which could potentially induce the GvL effect without causing GvFD. Availability: http://www.peptidecheck.org Contact: blasczyk.rainer@mh-hannover.de

Keywords

Minor Histocompatibility Antigens, Databases, Protein, Polymorphism, Single Nucleotide, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Average
Average
Top 10%
gold