Literature DB >> 11673239

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

R R Mallios1.   

Abstract

MOTIVATION: 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. AVAILABILITY: 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. CONTACT: ronna@ucsfresno.edu.

Mesh:

Substances:

Year:  2001        PMID: 11673239     DOI: 10.1093/bioinformatics/17.10.942

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  13 in total

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9.  A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach.

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Journal:  PLoS Comput Biol       Date:  2008-04-04       Impact factor: 4.475

10.  Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms.

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Journal:  BMC Bioinformatics       Date:  2007-11-22       Impact factor: 3.169

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