Shift-invariant adaptive double threading: learning MHC II - peptide binding

Authors:
Noah Zaitlen;Manuel Reyes-Gomez;David Heckerman;Nebojsa Jojic
Affiliations:
University of California San Diego, La Jolla, CA;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA
Venue:
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Year:
2007

Citing 2
Cited 2

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach

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Learning MHC I—peptide binding

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Multiple Instance Learning Allows MHC Class II Epitope Predictions Across Alleles

WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Using Gaussian Process with Test Rejection to Detect T-Cell Epitopes in Pathogen Genomes

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

Specificity of MHC binding to short peptide fragments from cellular as well as pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. The large variation in MHC class II epitope length has complicated training of predictors for binding affinities compared to MHC class I. In this paper, we treat the relative position of the peptide inside the MHC protein as a hidden variable, and model the ensemble of different binding configurations. The training procedure iterates the predictions with re estimation of the parameters of a binding groove model. We show that the model generalizes to new MHC class II alleles, which were not a part of the training set. To the best of our knowledge, our technique outperforms all previous approaches to MHC II epitope prediction. We demonstrate how our model can be used to explain previously documented associations between MHC II alleles and disease.