|Title||High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Borrman, T, Pierce, BG, Vreven, T, Baker, BM, Weng, Z|
|Date Published||2020 Dec 23|
MOTIVATION: The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.
RESULTS: Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.
AVAILABILITY AND IMPLEMENTATION: Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.