Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

R R Mallios

doi:10.1093/bioinformatics/15.6.432

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Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

R R Mallios

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Abstract

MOTIVATION: 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. AVAILABILITY: Software is available commercially; data are free over the Internet.

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Algorithms Amino Acid Sequence Binding Sites Computational Biology Databases, Factual Discriminant Analysis Epitopes HLA-DR1 Antigen Histocompatibility Antigens Class II Humans In Vitro Techniques Models, Biological Molecular Sequence Data Peptides Protein Binding Journal Article

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R R Mallios. 1999. Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.. https://doi.org/10.1093/bioinformatics%2F15.6.432

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