TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

Abstract

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

TITAN: T-cell receptor specificity prediction with bimodal attention networks

“…We emphasize that this is not an argument against our neural networks, but for the K-NN baseline model, which also outperforms the state of the art model, ImRex, a recent approach that uses 2D CNNs ( Moris et al, 2020 ) (see Section 3.4 for a more detailed comparison of TITAN and ImRex). We also note that an approach similar to our K-NN baseline, TCRMatch ( Chronister et al, 2020 ), was recently presented in a preprint. TCRMatch predicts TCR specificity using only sequence similarities to previously characterized receptors.…”

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

“…This combined database will provide a comprehensive list of receptor sequences and the epitopes they recognize. We have developed a ‘receptor lookup’ tool ( 58 ), which accepts the TCR β chain CDR3 sequence as an input, and identifies if TCRs with that exact sequence (or a highly similar one) have been previously characterized, and if so, what the previously identified epitope specificity is. This tool was designed to handle large input datasets, such as those generated by TCR repertoire sequencing experiments, and will annotate for each receptor if it has been found before and what epitopes it was previously reported to recognize in both cancer and other disease settings.…”

CD4+CCR6+ T cells dominate the BCG-induced transcriptional signature

“…To further examine the homogeneity of TCR repertoires before and after BCG vaccination, we used TCRMatch 31 to calculate similarity scores among the CDR3β sequences obtained from the subjects. CDR3β sequences obtained from each subject were separated into four groups depending on vaccination status at the time of sample collection and expansion status in response to BCG stimulation in vitro .…”

“…We also used TCRMatch to determine sequence similarity 31 . Within each group, defined by a single subject, vaccination and expansion status, and CDR3β sequences were tested against each other.…”