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High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides
Author(s) -
Tyler Borrman,
Brian G. Pierce,
Thom Vreven,
Brian M. Baker,
Zhiping Weng
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa1050
Subject(s) - t cell receptor , computational biology , in silico , antigen , computer science , t cell , major histocompatibility complex , immune system , biology , immunology , gene , biochemistry
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.

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