Genomewide Conserved Epitope Profiles of HIV-1 Predicted by Biophysical Properties of MHC Binding Peptides

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Abstract
We propose a new method for predicting MHC binding of peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and uses a model that simultaneously represents all the residues. The model discovers the quantified 9-mer "property model" within the longer peptides that are most common among binders. Prediction for a new peptide is based on its statistical "distance" from the extracted peptide property model. MHC-specific peptide property models were constructed from compiled binder/nonbinder data using this method. We report the results of cross-validation of the prediction method and comparison with other methods. The comparison suggests that our method performs substantially better for some MHC class II molecules and equally well for other MHC types. To demonstrate large-scale utility, 30 HIV-1 reference genomes covering diverse subtypes were analyzed. Regions that are likely to bind MHC (A2, DR1, or DR4) and that are conserved across the HIV-1 subtypes were identified. These "epitope profiles" of the diverse HIV-1 strains can also be visually presented to facilitate discovery of conserved patterns naturally occurring in the viral genomes. As an essential step in designing vaccines, the revealed patterns may provide valuable information in identifying the immunologically important regions.

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