A fingerprint approach to pioneer structure-based T cell receptor repertoire analysis and specificity prediction

The development of cancer immunotherapy has accelerated in recent years. Understanding the specificity of T cell receptors (TCR) for peptides presented by the major histocompatibility complex (pMHC) is a critical step towards improving immunotherapy approaches, such as adoptive cell transfer and peptide vaccination. Despite notable computational advances, the unambiguous pairing of TCR with pMHC, from pools of thousands of candidates and unseen pMHC, remains elusive.

To meet this challenge and showcase the potential of using physics-based structure-based methods without being hindered by their computational cost, we developed a novel approach, TCRfp. This method transforms the 3D structure of TCRs into one-dimensional structural fingerprints (FPs) using the electroshape 5D (ES5D) technique.

We have modelled more than 15'000 3D structures of paired TCR alpha and beta chains with known sequences and pMHC specificity and encoded them into 1D TCRfp. Anticipating future clinical applications, we have translated the TCR modelling process into a fast pipeline. Similarity measures between TCR FPs correlate with their ability to recognize similar or identical epitopes within both the training set and in the external validation sets.

TCRfp constitutes a rapid approach for high-throughput TCR comparison and repertoire analysis based on molecular 3D structures. When tested on a private dataset and combined with a basic sequence-based method via logistic regression, TCRfp surpassed existing approaches in predicting TCR specificities. TCRfp represents a structurally informed complement to sequence-based approaches and could enhance our ability to decode immune recognition.