Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm.

Abstract

Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design. An iterative stepwise discriminant analysis meta-algorithm is utilized to derive a quantitative motif for classifying potential agretopes as high-, moderate- or non-binders for HLA-DR1, a class II MHC molecule. A large molecular online database provides the input for this data-driven algorithm. The model correctly classifies over 85% of the peptides in the database. Stepwise discriminant analysis software is available commercially in SPSS and BMDP statistical software packages. Peptides known to bind MHC molecules can be downloaded from http://wehih.wehi.edu.au/mhcpep/. Peptides known not to bind HLA-DR1 are available from the author upon request. ronna@ucsfresno.edu.