TL;DR
DynaTPH 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.
It is demonstrated that conventional peptide-level hydrogen–deuterium exchange mass spectrometry (HDX-MS) workflows can identify productive TCR engagement with their cognate pHLAs and shows that TCRs that bind centrally within the pHLA groove have distinct uptake profiles compared with those adopting alternative orient...
Protease-activated receptor 1 (PAR1) is a class A G protein-coupled receptor activated by proteolytic cleavage and exposure of a tethered agonist. While existing structures have revealed inactive and active conformations, the ligand-dependent conformational energy landscape governing PAR1 activation remains poorly un...
Understanding the dynamic behavior of fuzzy complexes formed by intrinsically disordered proteins (IDPs) is challenging due to their conformational flexibility. Nuclear Magnetic Resonance (NMR) structural bundles satisfy experimental constraints and inherently provide an overview of alternative conformations in solutio...
Antigens of intracellular origin are processed then presented by proteins of the highly polymorphic major histocompatibility complex I (MHCI), thereby enabling T cell activation during an immune response. In particular, the peptide-exchange catalyst tapasin (Tsn) plays a critical role in shaping the pool of peptide ant...
The ML model (DynamiT), trained on simulation-derived structural and dynamical features extracted from 2500 time points, revealed key determinants responsible for T-cell activation with an accuracy of 73.3%.
Evaluation across multiple settings indicates that TCR-IFNet achieves competitive performance compared with existing methods, with higher AUPRC observed on both antigen-specific and healthy-sourced datasets, as well as improved results on independent test sets.
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