The above calculation suggests that a single T cell receptor can recognize as many as 10 6 ligands. How related are these ligands, and is the cross-reactivity of a T cell clone predictable? A few studies suggest that cross-reactivity is not completely random [9, 16] , while others argue that T cells can recognize unrelated ligands (see e.g. [17] ). Here, we investigate whether TCR peptide cross-recognition can be predicted by a quantitative model of peptide similarity using amino acid similarity matrices (SM) as explained in detail in Materials and Methods. The peptide similarity score is unity for two identical peptides, and 0 for peptides of maximum dissimilarity, as defined by the SM. Note, that this simple model does not differentiate between positions. Below, the predictions made from this model are tested on several independent data sets, and compared against the performance of random predictors. [10] ). The Pearson correlation coefficients between their relative SFU and our peptide similarity score were: 0.40, 0.39, and 0.35 for G10, T4, and PBMC data, respectively (p,0.0001, Monte Carlo randomization exact estimate). Since PBMC consist of two clones, where one clone is dominant [10] , the prediction performance on this data set is similar to the performance on the single clonal data. These significant correlation coefficients suggest that peptide crossreactivity can, to some degree, be estimated from peptide similarities. Thus the proposed model of peptide similarity is capable of producing significant predictions of the loss of recognition due to single amino acid substitutions. Iversen et al. [18] measured IFNc secretion by T cells specific for SLYNT-VATL (SYL9), when they are stimulated with naturally occurring (i.e, patient derived) variants of SYL9. Data consisted of 21 variants of SYL9. Each variant peptide had between 1 and 3 mutations with respect to SYL9. Fig. 2B presents a scatter plot of the data from Iversen et al. [18] for the T4 clone, where the peptide similarity is plotted on the y-axis against the relative IFNc secretion (x-axis). Using the BLOSUM35 matrix to calculate the peptide similarity score (see Materials and Methods) the Pearson correlation was 0.65. Similar results were obtained using BLOSUM matrices 35-90 (data not shown). For the remaining CTL clones (G10, C-3, C-4, C-22 and C-32) tested by Iversen et al. [18] correlations were 0.49, 0.47, 0.55, 0.60 and 0.57 respectively (all values are significantly different from zero with p,0.02 Monte Carlo randomization exact estimate). This model of peptide similarity (or cross-reactivity) was thus able to explain around 20240% of the IFNc secretion. Still, a number of SYL9 variants, for which we predict rather high peptide similarity to SYL9, hardly induce an IFNc response, e.g., A7S, A7V, T5A mutants given in the upper left corner of Fig. 2 . Part of this discrepancy is due to the fact that our model is not position specific, and thus underestimates the effect of mutations in the central positions, which are crucial for T cell recognition (see Fig. 1A ). When more data becomes available, the peptide similarity model can be extended with a weighting accounting for the relative importance of the peptide positions.