BRIEF RESEARCH REPORT article
Front. Immunol.
Sec. T Cell Biology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1621201
This article is part of the Research TopicAdvancing T Cell Biology: Novel Insights into Epitope Recognition and Immune Response DynamicsView all 4 articles
T-cell receptors that are k-binding have defined sequence features
Provisionally accepted- Massachusetts Institute of Technology, Cambridge, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Previous studies have revealed that individual T cell receptors (TCRs) can recognize a diverse set of peptide targets displayed by Major Histocompatibility Complexes (MHCs) to enable effective adaptive immune surveillance. However, how TCR sequences encode their cross-reactivity remains poorly understood. Here, we used an in vitro assay to characterize the k-binding of 196 (~47 million) different TCRs in the context of a single TCR framework for binding to seven related peptides displayed by HLA-A*02:01. We define k-binding to be the number of peptide-MHC targets recognized by a TCR within a specific universe of targets. We found a hierarchy of TCR complementarity-determining region 3 (CDR3) alpha and beta chain residue importance that determined k-binding for the seven targets. Our machine learning model that embedded TCR sequences using BLOSUM-50 provided an overall F1 score of 0.698 and an AUPRC of 0.745 for predicting TCR-pMHC binding, which was significantly superior to model results from VHSE-8 embedded or one hot encoded sequences. When we used our model to predict observed k-binding, we found that experimentally derived sequence motifs do not fully explain the relative importance of different CDR3 residues. We determined CDR3 residue importance by examining the reduction in machine learning model predictive ability by masking individual CDR3 residues. We found that the resulting residue importance ranking was significantly correlated to residue importance determined with a computational alanine scan using Rosetta. Our findings validate past theoretical predictions of TCR cross-reactivity and demonstrate that TCRs used in therapeutics must be carefully evaluated for their specificity.
Keywords: Immunological specificity, tcr, T-cell receptor, TCR cross-reactivity, MHC class I, HLA-A allotype, HLA-A*02:01
Received: 30 Apr 2025; Accepted: 19 Sep 2025.
Copyright: © 2025 Park, Krog, Carter, Birnbaum and Gifford. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David K Gifford, gifford@mit.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.