Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.
BACKGROUND
The identification of T-cell epitopes can be crucial for vaccine development. An epitope is a peptide segment that binds to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. Predicting which peptide segments bind MHC molecules is the first step in epitope prediction.


RESULTS
An iterative stepwise discriminant analysis meta-algorithm explores a large molecular database to derive quantitative motifs for peptide binding. The applications presented here demonstrate the algorithm's versatility by producing four closely related models for HLA-DR1. Two models use an expert initial estimate and two do not; two models use amino acid residues as the only predictors and two use amino acid groupings as additional predictors. Each model correctly classifies >90% of the peptides in the database.


BACKGROUND
Software is available commercially; data are free over the Internet.
Mallios
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Medical Information Resources, University of California, San Francisco, 2615 East Clinton Avenue, Fresno, CA 93703, USA. ronna@ucsfresno.edu
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology; D016208:Databases, Factual; D016002:Discriminant Analysis; D000939:Epitopes / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D015798:HLA-DR1 Antigen / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D006801:Humans; D066298:In Vitro Techniques; D008954:Models, Biological; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D011485:Protein Binding
D000939:Epitopes; D015798:HLA-DR1 Antigen; D000949:Histocompatibility Antigens Class II; D010455:Peptides