Prediction of immunodominant helper T cell antigenic sites from the primary sequence.

Abstract

Abstract We have used a data base of 23 known immunodominant helper T cell antigenic sites located on 12 proteins to systematically develop an optimized algorithm for predicting T cell antigenic sites. The algorithm is based on the amphipathic helix model in which antigenic sites are postulated to be helices with one face predominantly polar and the opposite face predominantly apolar. Such amphipathic structures can form when the polarity of residues along the sequence varies with a more or less regular period. Hence they can be identified by methods (so called power spectrum procedures) that detect periodic variations in properties of a sequence. The choice of power spectrum procedure, hydrophobicity scale, and model parameters are examined. An algorithm is tested by comparing the predicted amphipathic segments with the locations of the known T cell sites, counting the number of matches, and calculating the probability of getting this number by chance alone. The optimum algorithm, which predicts the largest number of sites with the lowest chance probability, uses the Fauchere-Pliska hydrophobicity scale and a least squares fit of a sinusoid as its power spectrum procedure. By applying this algorithm, 18 of the 23 known sites are identified (75% sensitivity) with a high degree of significance (p less than 0.001). The success of the algorithm supports the hypothesis that stable amphipathic helices are fundamentally important in determining immunodominance. This approach may be of practical value in designing synthetic vaccines aimed at T cell immunity.