Abstract

Understanding how T cell receptors (TCRs) recognize peptides presented by class-I major histocompatibility complexes (pMHCs) is a central challenge in immunology, with implications for infectious disease research, autoimmunity, and the development of personalized cancer immunotherapies. While recent advances in 3D protein structure prediction have enabled more accurate modeling of protein complexes, current methods fail to capture the flexible nature of TCR-pMHC interactions. Due to the high flexibility in the CDR loops of the TCR and the potential flexibility of the peptide, these interactions are dynamic and can adopt a range of energetically favorable binding modes that are not well represented by a single static structure. We introduce STEGG: Structural TCR-pMHC Ensemble Generator and Gallery, a computational framework for generating structural ensembles of TCR-pMHC complexes from amino acid sequences. STEGG generates a range of distinct low-energy 3D conformations for each TCR-pMHC pair, capturing the diversity and flexibility of TCRs, pMHCs, and their joint interactions. Unlike conventional molecular dynamics simulations, which are computationally expensive and scale poorly, STEGG utilizes a domain specific sampling algorithm, enabling efficient 3D modeling. We show that STEGG not only recovers conformations with low RMSD to experimentally solved structures but also finds a diverse range of biologically relevant binding modes and flexible poses. To support further research, we provide a publicly available database of structural ensembles for TCR-pMHC pairs. By going beyond static structure generation, STEGG presents a new method for studying the molecular drivers of TCR-pMHC interactions. The STEGG web server is freely available at https://stegg.kavrakilab.rice.edu/.

Publisher: http://dx.doi.org/https://doi.org/10.1016/j.jmb.2026.169762

PDF preprint: http://kavrakilab.org/publications/slone_stegg_2026.pdf