title: Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores.
abstract: BACKGROUND
  Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel.
  
  
  RESULTS
  The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP 1, MCHBN 2, and MHCBench 3. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database.
  
  
  CONCLUSIONS
  The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.
authors.affiliation: The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK. bio@salomons.dk
authors.forename: Jesper
authors.initials: J
authors.lastname: Salomon
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D001665:Binding Sites; D019295:Computational Biology; D030541:Databases, Genetic; D018604:Epitope Mapping; D015234:HLA-A Antigens / Q000737:chemistry / Q000378:metabolism; D006684:HLA-DR Antigens / Q000737:chemistry / Q000378:metabolism; D059811:HLA-DRB1 Chains; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D012372:ROC Curve; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D017386:Sequence Homology, Amino Acid
chemical_list: D015234:HLA-A Antigens; D006684:HLA-DR Antigens; D059811:HLA-DRB1 Chains; D000949:Histocompatibility Antigens Class II; D010455:Peptides