High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides.

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TitleHigh-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides.
Publication TypeJournal Article
Year of Publication2020
AuthorsBorrman, T, Pierce, BG, Vreven, T, Baker, BM, Weng, Z
Date Published2020 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.

Alternate JournalBioinformatics
PubMed ID33355667