Limits on Inferring T-cell Specificity from Partial Information

TL;DR

This paper quantifies the information content of T-cell receptor features in determining antigen specificity, revealing key regions and providing bounds for prediction accuracy, with implications for machine learning and cell therapy optimization.

Contribution

It introduces a statistical framework using coincidence-based information measures to quantify TCR specificity from partial sequence data, advancing predictive modeling.

Findings

TCR specificity depends on hypervariable regions of both receptor chains.

The degree of synergy in specificity varies with the ligand.

Bounds on prediction accuracy from partial sequence matches are established.

Abstract

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.