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


RESULTS
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.


BACKGROUND
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.


BACKGROUND
ronna@ucsfresno.edu.
Mallios
R R
RR
Office of Sponsored Projects and Research, University of California, San Francisco, 2615 East Clinton Avenue, Fresno, CA 93703, USA. ronna@ucsfresno.edu
D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D016002:Discriminant Analysis; D015798:HLA-DR1 Antigen / Q000378:metabolism; D000949:Histocompatibility Antigens Class II / Q000378:metabolism*; D006801:Humans; D010455:Peptides / Q000235:genetics* / Q000378:metabolism*; D011485:Protein Binding; D012984:Software
D015798:HLA-DR1 Antigen; D000949:Histocompatibility Antigens Class II; D010455:Peptides