UMass Chan Medical School Faculty Publications


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

UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Publication Date


Document Type



Amino Acids, Peptides, and Proteins | Bioinformatics | Immune System Diseases | Structural Biology


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

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI of Published Version



Borrman T, Pierce BG, Vreven T, Baker BM, Weng Z. High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides. Bioinformatics. 2020 Dec 23:btaa1050. doi: 10.1093/bioinformatics/btaa1050. Epub ahead of print. PMID: 33355667. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Bioinformatics (Oxford, England)

PubMed ID