Shift-Invariant Adaptive Double Threading: Learning MHC II - Peptide Binding

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.

[1]  M F del Guercio,et al.  Several common HLA-DR types share largely overlapping peptide binding repertoires. , 1998, Journal of immunology.

[2]  O. Schueler‐Furman,et al.  Structure‐based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC alleles , 2000, Protein science : a publication of the Protein Society.

[3]  Søren Brunak,et al.  Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach , 2004, Bioinform..

[4]  Tomer Hertz,et al.  PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions , 2006, BMC Bioinformatics.

[5]  H. Rammensee,et al.  SYFPEITHI: database for MHC ligands and peptide motifs , 1999, Immunogenetics.

[6]  Philip E. Bourne,et al.  Curation of complex, context-dependent immunological data , 2006, BMC Bioinformatics.

[7]  J. Drijfhout,et al.  HLA-DR binding analysis of peptides from islet antigens in IDDM. , 1998, Diabetes.

[8]  Gajendra P. S. Raghava,et al.  MHCBN: a comprehensive database of MHC binding and non-binding peptides , 2003, Bioinform..

[9]  Yang Dai,et al.  Prediction of MHC class II binding peptides based on an iterative learning model , 2005, Immunome research.

[10]  Jan Engberg,et al.  Visualization of Myelin Basic Protein (Mbp) T Cell Epitopes in Multiple Sclerosis Lesions Using a Monoclonal Antibody Specific for the Human Histocompatibility Leukocyte Antigen (Hla)-Dr2–Mbp 85–99 Complex , 2000, The Journal of experimental medicine.

[11]  Derin B Keskin,et al.  Peptide 15-mers of defined sequence that substitute for random amino acid copolymers in amelioration of experimental autoimmune encephalomyelitis. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  A. Sali,et al.  Statistical potentials for fold assessment , 2009 .

[13]  Vladimir Brusic,et al.  MHCPEP, a database of MHC-binding peptides: update 1996 , 1997, Nucleic Acids Res..

[14]  D. T. Jones,et al.  A new approach to protein fold recognition , 1992, Nature.

[15]  Claude Beazley,et al.  A Novel Predictive Technique for the MHC Class II Peptide-Binding Interaction , 2003, Molecular medicine.

[16]  Vladimir Brusic,et al.  MHCPEP, a database of MHC-binding peptides: update 1996 , 1997, Nucleic Acids Res..

[17]  Celia A Schiffer,et al.  Lack of synergy for inhibitors targeting a multi‐drug‐resistant HIV‐1 protease , 2002, Protein science : a publication of the Protein Society.

[18]  Wilfred W. Li,et al.  MEME: discovering and analyzing DNA and protein sequence motifs , 2006, Nucleic Acids Res..

[19]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[20]  Ellis L. Reinherz,et al.  Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles , 2004, Immunogenetics.

[21]  Ora Schueler-Furman,et al.  Learning MHC I - peptide binding , 2006, ISMB.