| Abstract
| - The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology.As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex(MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes.The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developedbioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS methodovercomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity,such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site.The method has applications in both the accurate prediction of class II epitopes and the manipulation ofaffinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles(I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series ofregression equations highlighting the quantitative contributions of individual amino acids at each peptideposition was established. The initial model for each allele exhibited only moderate predictivity. Once theset of selected peptide subsequences had converged, the final models exhibited a satisfactory predictivepower. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical termsq2, SEP, and NCranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database(http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecularmodeling software package. The resulting models, which can be used for accurate T-cell epitope prediction,will be made freely available online (http://www.jenner.ac.uk/MHCPred).
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