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

Tyler Borrman; Brian G Pierce; Thom Vreven; Brian M Baker; Zhiping Weng

doi:10.1093/bioinformatics/btaa1050

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

Bioinformatics. 2021 Apr 1;36(22-23):5377-5385. doi: 10.1093/bioinformatics/btaa1050.

Authors

Tyler Borrman¹, Brian G Pierce^{2

3}, Thom Vreven¹, Brian M Baker^{4

5}, Zhiping Weng¹

Affiliations

¹ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
² University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA.
³ Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
⁴ Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
⁵ Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA.

Abstract

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/weng-lab/antigen-predict.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

Grants and funding