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Elucidating the cellular immune response to infectious agents is a prerequisite for understanding
disease pathogenesis and designing effective vaccines. In the identification of microbial T-cell epitopes, the availability of purified or recombinant bacterial proteins has been a chief limiting factor. In chronic infectious diseases such as
Lyme disease, immune-mediated damage may add to the effects of direct infection by means of molecular mimicry to tissue autoantigens. Here, we describe a new method to effectively identify both microbial epitopes and candidate autoantigens. The approach combines data acquisition by positional scanning peptide combinatorial libraries and biometric data analysis by generation of scoring matrices. In a patient with chronic neuroborreliosis, we show that this strategy leads to the identification of potentially relevant T-cell targets derived from both Borrelia burgdorferi and the host. We also found that the antigen specificity of a single T-cell clone can be degenerate and yet the clone can preferentially recognize different peptides derived from the same organism, thus demonstrating that flexibility in T-cell recognition does not preclude specificity. This approach has potential applications in the identification of ligands in infectious diseases, tumors and autoimmune diseases.