A computational pipeline to generate MHC binding motifs

Background Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. Results In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. Conclusions We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.

[1]  Alessandro Sette,et al.  The Immune Epitope Database 2.0 , 2009, Nucleic Acids Res..

[2]  Clemencia Pinilla,et al.  Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior , 2009, BMC Bioinformatics.

[3]  Morten Nielsen,et al.  NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction , 2009, BMC Bioinformatics.

[4]  Morten Nielsen,et al.  MHC motif viewer , 2008, Immunogenetics.

[5]  Bjoern Peters,et al.  Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries , 2008, Immunome research.

[6]  Allison L. Zajac,et al.  Genome-Wide Analysis Reveals a Highly Diverse CD8 T Cell Response to Murine Cytomegalovirus , 2006 .

[7]  H. Ertl,et al.  Identification of mouse AAV capsid-specific CD8+ T cell epitopes. , 2005, Molecular therapy : the journal of the American Society of Gene Therapy.

[8]  Alessandro Sette,et al.  Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method , 2005, BMC Bioinformatics.

[9]  Bjoern Peters,et al.  Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications , 2005, Immunogenetics.

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

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

[12]  G. Crooks,et al.  WebLogo: a sequence logo generator. , 2004, Genome research.

[13]  Jörg Schultz,et al.  HMM Logos for visualization of protein families , 2004, BMC Bioinformatics.

[14]  O. Lund,et al.  novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .

[15]  Arne Elofsson,et al.  Prediction of MHC class I binding peptides, using SVMHC , 2002, BMC Bioinformatics.

[16]  H. Rammensee,et al.  The Role of Peptides in T Cell Alloreactivity Is Determined by Self–Major Histocompatibility Complex Molecules , 2000, The Journal of experimental medicine.

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

[18]  K. Takase,et al.  [T cell activation]. , 1995, Ryumachi. [Rheumatism].

[19]  D. Madden.  The three-dimensional structure of peptide-MHC complexes. , 1995, Annual review of immunology.

[20]  D. Wiley,et al.  Importance of peptide amino and carboxyl termini to the stability of MHC class I molecules. , 1994, Science.

[21]  H. Rammensee,et al.  Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules , 1991, Nature.

[22]  T. D. Schneider,et al.  Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.