  T cell receptors (TCRs) are critical components of adaptive immune systems,
responsible for responding to threats by recognizing epitope sequences
presented on host cell surface. Computational prediction of binding affinity
between TCRs and epitope sequences using machine/deep learning has attracted
intense attention recently. However, its success is hindered by the lack of
large collections of annotated TCR-epitope pairs. Annotating their binding
affinity requires expensive and time-consuming wet-lab evaluation. To reduce
annotation cost, we present ActiveTCR, a framework that incorporates active
learning and TCR-epitope binding affinity prediction models. Starting with a
small set of labeled training pairs, ActiveTCR iteratively searches for
unlabeled TCR-epitope pairs that are ''worth'' for annotation. It aims to
maximize performance gains while minimizing the cost of annotation. We compared
four query strategies with a random sampling baseline and demonstrated that
ActiveTCR reduces annotation costs by approximately 40%. Furthermore, we showed
that providing ground truth labels of TCR-epitope pairs to query strategies can
help identify and reduce more than 40% redundancy among already annotated pairs
without compromising model performance, enabling users to train equally
powerful prediction models with less training data. Our work is the first
systematic investigation of data optimization for TCR-epitope binding affinity
prediction.
