Limits on Inferring T-cell Specificity from Partial Information

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.

24 pages, 15 figures

References in corpus (11)

• A Simple Framework for Contrastive Learning of Visual Representations
• The information bottleneck method
• A Review of Feature Selection Methods Based on Mutual Information
• OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs
• Optimal decoding of information from a genetic network
• How a well-adapted immune system is organized
• Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
• Genesis of the alpha beta T-cell receptor
• Inference of entropies of discrete random variables with unknown cardinalities
• Generalized Shannon-Khinchin Axioms and Uniqueness Theorem for Pseudo-additive Entropies
• Finding the last bits of positional information

Cited by in corpus (1)

• Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules