TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions

Comprehensive and integrated resources on interactions between T-cell receptors (TCRs) and antigens are still lacking for adoptive T-cell-based immunotherapies, highlighting a significant gap that must be addressed to fully understand the mechanisms of antigen recognition by T cells. In this study, we present the T-cell receptor-antigen interaction database (TRAIT), a comprehensive database that profiles the interactions between TCRs and antigens. TRAIT stands out due to its comprehensive description of TCR-antigen interactions by integrating sequences, structures, and affinities. It provides millions of experimentally validated TCR-antigen pairs, resulting in an exhaustive landscape of antigen-specific TCRs. Notably, TRAIT emphasizes single-cell omics as a major reliable data source for TCR-antigen interactions and includes millions of reliable non-interactive TCRs. Additionally, it thoroughly demonstrates the interactions between mutations of TCRs and antigens, thereby benefiting affinity optimization of engineered TCRs as well as vaccine design. TCRs on clinical trials are innovatively provided. With the significant efforts made toward elucidating the complex interactions between TCRs and antigens, TRAIT is expected to ultimately contribute superior algorithms and substantial advancements in the field of T-cell-based immunotherapies. TRAIT is freely accessible at https://pgx.zju.edu.cn/traitdb.

Authors

Institutions

Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2025-04-21
DOI
https://doi.org/10.1093/gpbjnl/qzaf033
Citations
4
Primary Topic
CAR-T cell therapy research
Type
article
Field-Weighted Citation Impact
4.18

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions

Shuqing Chen, Xue Zhang, Yichang Chen, Zhan Zhou et al.
4 citations
Genomics Proteomics & Bioinformatics
CAR-T cell therapy research
4.18
article

TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions

Shuqing Chen, Xue Zhang, Yichang Chen, Zhan Zhou, Gang Pan, Feng Zhu, Wenyi Zhao, Yuxuan Zhou, Ying Chi, Jingcheng Wu, Shuxian Bai, M. Wei
article en
4 citations

Abstract

Comprehensive and integrated resources on interactions between T-cell receptors (TCRs) and antigens are still lacking for adoptive T-cell-based immunotherapies, highlighting a significant gap that must be addressed to fully understand the mechanisms of antigen recognition by T cells. In this study, we present the T-cell receptor-antigen interaction database (TRAIT), a comprehensive database that profiles the interactions between TCRs and antigens. TRAIT stands out due to its comprehensive description of TCR-antigen interactions by integrating sequences, structures, and affinities. It provides millions of experimentally validated TCR-antigen pairs, resulting in an exhaustive landscape of antigen-specific TCRs. Notably, TRAIT emphasizes single-cell omics as a major reliable data source for TCR-antigen interactions and includes millions of reliable non-interactive TCRs. Additionally, it thoroughly demonstrates the interactions between mutations of TCRs and antigens, thereby benefiting affinity optimization of engineered TCRs as well as vaccine design. TCRs on clinical trials are innovatively provided. With the significant efforts made toward elucidating the complex interactions between TCRs and antigens, TRAIT is expected to ultimately contribute superior algorithms and substantial advancements in the field of T-cell-based immunotherapies. TRAIT is freely accessible at https://pgx.zju.edu.cn/traitdb.

Genomics Proteomics & BioinformaticsVol. 23(3)
Zhejiang Lab (CN), Zhejiang University-University of Edinburgh Institute (CN), Artificial Intelligence in Medicine (Canada) (CA), Alibaba Group (China) (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Zhejiang University, Natural Science Foundation of Zhejiang Province
Openalex Percentile: Top 6%
CAR-T cell therapy research
4.18
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.