A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics
Zhang, S.; Long, Y.; Wang, T.; Zhong, Q.; Li, J.; Fu, L.
Show abstract
Dynamic interactions between T cell receptor (TCR) and peptide-human leukocyte antigen (pHLA) complexes are central to peptide-specific immune recognition, influencing T cell activation and immune responses. While structural biology has provided valuable static structures of TCR-pHLA complexes, systematic datasets capturing their dynamic and interaction patterns remain limited. Here, we present DynaTPH, a curated structural dynamics dataset of human TCR-pHLA complexes. DynaTPH integrates TCR-pHLA structures, covering both HLA class I and class II complexes, and extends these static structural resources with standardized molecular dynamics simulations and derived biophysical properties. Through a multi-stage filtering procedure, we identified 256 representative complexes and performed standardized all-atom molecular dynamics simulations for each system, corresponding to a cumulative simulation time of 38.4 s. The dataset includes static structures, trajectories, corresponding frames, and derived physicochemical properties, including hydrogen bonds, intermolecular contacts, solvent accessibility, and backbone flexibility. By capturing the conformational flexibility and dynamic interaction patterns across diverse TCR-pHLA interfaces, DynaTPH extends static structural resources with multidimensional biophysical information. This dataset enables systematic investigation of TCR-pHLA recognition dynamics and supports applications in TCR engineering, vaccine design, and immune tolerance research and artificial intelligence-driven computational immunology.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Structure-based prediction of T cell receptor:peptide-MHC interactions 94%
- The flexible stalk domain of sTREM2 modulates its interactions with brain-based phospholipids 94%
- Revealing druggable cryptic pockets in the Nsp-1 of SARS-CoV-2 and other β-coronaviruses by simulations and crystallography 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.