Abstract
AbstractT cells mediate immune responses against pathogens and cancer through T cell receptors (TCRs) that recognize foreign peptides presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. TCRs carry enormous diversity and differ across individuals, and mechanisms that determine TCR-pMHC binding are poorly understood. The ability to predict TCR-pMHC interactions would accelerate development of cellular therapeutics to design TCRs that specifically bind to a target of interest. We designed a randomized library of 196TCR CDR3βsequences and experimentally evaluated their affinities for the Tax-A02 peptide-MHC target. We trained ML models that predict TCR binding to Tax-A02 from TCR sequence and used model interpretation to identify TCR sequence features associated with binding. We found these features accurately mirror the true sequence features in our experimental data.
Publisher
Cold Spring Harbor Laboratory
Reference20 articles.
1. Identification and engineering of human variable regions that allow expression of stable single-chain T cell receptors
2. Deconstructing the Peptide-MHC Specificity of T Cell Recognition
3. Carter, B. , Mueller, J. , Jain, S. , and Gifford, D. What made you do this? Understanding black-box decisions with sufficient input subsets. In The 22nd International Conference on Artificial Intelligence and Statistics, pp. 567–576, 2019.
4. Critiquing protein family classification models using sufficient input subsets;Journal of Computational Biology,2020
5. Davison, A. C. and Hinkley, D. V. Bootstrap methods and their application, pp. 140–141. Number 1. Cambridge University Press, 1997.