{
  "abstract": "Background Peptide-HLA (pHLA) interactions drive cytotoxic T-cell responses and are the foundation of cancer immunotherapy, yet the structural characterization of the pHLA complexes, so necessary to the development of such therapies, remains severely limited by the scarcity of high-resolution experimental structures. This gap hampers efforts in neoantigen discovery, vaccine development, and precision immunotherapy design.Methods We developed HLA-Arena 2.0 ( figures 1 and 2), a web-accessible platform designed to streamline and scale the modeling, analysis, and visualization of pHLA complexes. The platform uses a modular, container-based architecture, enabling users to select specific tools as needed and run workflows either in Google Colab or locally when handling sensitive data—no programming experience required. HLA-Arena 2.0 supports end-to-end neoantigen discovery and prioritization, integrating tools like pVACtools1 for epitope prediction, CrossDome2 for off-target toxicity assessment, and structure-based modeling with APE-Gen3 and DINC.4 Rescoring with Smina5 and Ref20156 is also available, as an upgraded scoring module enables more accurate peptide ranking based on predicted affinity and structural stability. The platform’s visual workflow builder and notebook export feature ensure both flexibility and reproducibility.Results HLA-Arena 2.0 enables rapid modeling and comparison of pHLA complexes, offering built-in workflows for virtual screening, binding prediction, and structure-based benchmarking against crystal structures. These integrated tools improve the identification of immunogenic peptides that may be overlooked by sequence-based predictors alone. The visual interface facilitates broader adoption among experimental researchers and ensures reproducibility via notebook export. Moreover, the inclusion of CrossDome allows users to systematically assess potential cross-reactivity risks, which is crucial for minimizing off-target effects in clinical applications.Conclusions By integrating key components of the neoantigen discovery workflow—including sequence-based predictions, structural modeling, and off-target risk assessment—into a flexible, no-code platform, HLA-Arena 2.0 democratizes access to high-quality structure-based immunoinformatics. This user-friendly system lowers the barrier for both computational and experimental researchers to perform structure-guided analyses, enabling more confident peptide prioritization for cancer immunotherapy. By streamlining virtual screening, benchmarking, and peptide selection within a reproducible framework, HLA-Arena 2.0 supports the development of safer, more effective pHLA-targeted interventions.Acknowledgements Research reported in this abstract was supported by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI/NIH) to the University of Houston under Award Number 1R21CA289333-01.References Hundal J, Kiwala S, McMichael J, et al. pVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunol Res. 2020;8:292–305.Fonseca A, Antunes D. CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases. Front Immunol. 2023;14:1142573.Abella J, Antunes D, Clementi C, et al. APE-Gen: a fast methods for Generating Ensembles of Bound Peptide-MHC Conformations. Molecules. 2019;24:881.Antunes D, Moll M, Devaurs D, et al. DINC 2.0: a new protein-peptide docking webserver using an incremental approach. Cancer Res. 2017;77:e55-e57.Koes D, Baumgartner M, Camacho C. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model. 2013;53:1893–1904.Alford R, Leaver-Fay A, Jeliazkov J, et al. The rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput. 2017;13:3031–3048.Abstract 1128 Figure 1Overview of the HLA-Arena 2.0 platform. The HLA-Arena 2.0 homepage presents a user-friendly, no-code platform for modeling and analyzing pHLA complexes, supporting geometry prediction, binding, screening, and custom workflowsAbstract 1128 Figure 2Visual workflow builder for modular pipeline creation. The visual workflow builder allows users to select tools for pHLA modeling, docking, rescoring, and visualization, generating reproducible Jupyter notebooks for local or Colab execution",
  "authors": [
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Martiela Vaz de Freitas"
    },
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Dinler Antunes"
    }
  ],
  "title": "1128 HLA-Arena 2.0: expanding access to pHLA structural modeling and peptide prioritization for precision immunotherapy",
  "uid": "fc096717-28f8-54f6-9c64-91698855cd58"
}