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Issue 22, 2004
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New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

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Abstract

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptideprotein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Graphical abstract: New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

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Publication details

The article was received on 25 Jun 2004, accepted on 11 Aug 2004 and first published on 16 Sep 2004


Article type: Paper
DOI: 10.1039/B409656H
Org. Biomol. Chem., 2004,2, 3274-3283

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    New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

    C. K. Hattotuwagama, P. Guan, I. A. Doytchinova and D. R. Flower, Org. Biomol. Chem., 2004, 2, 3274
    DOI: 10.1039/B409656H

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