TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning

Rui Yin; Helder V Ribeiro-Filho; Valerie Lin; Ragul Gowthaman; Melyssa Cheung; Brian G Pierce

doi:10.1093/nar/gkad356

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning

Nucleic Acids Res. 2023 Jul 5;51(W1):W569-W576. doi: 10.1093/nar/gkad356.

Authors

Rui Yin^{1

2}, Helder V Ribeiro-Filho^{1

3}, Valerie Lin^{1

4}, Ragul Gowthaman^{1

2}, Melyssa Cheung^{1

5}, Brian G Pierce^{1

2

6}

Affiliations

¹ University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA.
² Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
³ Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil.
⁴ Thomas S. Wootton High School, Rockville, MD 20850, USA.
⁵ Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA.
⁶ University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA.

Abstract

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Antigens
Computer Simulation
Deep Learning*
Humans
Peptides / chemistry
Receptors, Antigen, T-Cell / chemistry

Substances

Receptors, Antigen, T-Cell
Peptides
Antigens

Grants and funding

R35 GM144083/GM/NIGMS NIH HHS/United States