  A key challenge in molecular biology is to decipher the mapping of protein
sequence to function. To perform this mapping requires the identification of
sequence features most informative about function. Here, we quantify the amount
of information (in bits) that T-cell receptor (TCR) sequence features provide
about antigen specificity. We identify informative features by their degree of
conservation among antigen-specific receptors relative to null expectations. We
find that TCR specificity synergistically depends on the hypervariable regions
of both receptor chains, with a degree of synergy that strongly depends on the
ligand. Using a coincidence-based approach to measuring information enables us
to directly bound the accuracy with which TCR specificity can be predicted from
partial matches to reference sequences. We anticipate that our statistical
framework will be of use for developing machine learning models for TCR
specificity prediction and for optimizing TCRs for cell therapies. The proposed
coincidence-based information measures might find further applications in
bounding the performance of pairwise classifiers in other fields.
