AutoHMD: Scalable and Accurate Validation of Antibody–Antigen Complexes by Heated Molecular Dynamics

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ID: 331947
2026
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Abstract
Abstract Motivation Antibody–antigen (Ab–Ag) interactions are central to immune recognition and widely exploited in therapeutic design, making accurate structural characterization of these complexes a central challenge in the field. While docking and artificial intelligence–based approaches can generate multiple candidate binding modes, reliably identifying near–native conformations represent a key bottleneck. Existing methods depend largely on static scoring functions, which fail to capture the dynamic nature of Ab–Ag interfaces and, as a result, offer limited power to discriminate between competing poses. Results Here, we evaluate heated molecular dynamics (HMD) as a strategy to discriminate binding pose quality based on structural stability under thermal perturbation, implemented through the automated workflow AutoHMD. Across a diverse set of Ab–Ag complexes, HMD enabled clear separation between high–quality and incorrect poses. During simulations, near–native complexes maintained a stable interface, whereas incorrect ones showed progressive divergence, particularly at elevated temperatures. Interaction analysis revealed that high-quality poses maintain more stable and persistent interfacial contacts throughout the simulations. In contrast, incorrect poses display an unstable interaction network. Additional simulation replicates increased classification consistency, while a mean iRMSD cutoff of approximately 2.5 Å achieved 90% accuracy in cross-validation and retained robust performance on an independent external set. Its transfer to Boltz-2-generated complexes further supported the broader applicability of HMD to AI-predicted binding poses. Overall, AutoHMD provides a reproducible, scalable, and high–throughput framework for validation of predicted antibody–antigen complexes, with potential applications in structural bioinformatics and biopharmaceutical design. Supplementary information Supplementary data are available at Bioinformatics online. The AutoHMD workflow scripts are available on GitHub at https://github.com/SFBBGroup/AutoHMD. The version used in this study is permanently archived in Zenodo at https://doi.org/10.5281/zenodo.22757672. All simulation and structural data supporting this study are publicly available in Zenodo at https://doi.org/10.5281/zenodo.20722229; https://doi.org/10.5281/zenodo.22750139; https://doi.org/10.5281/zenodo.20707612.
Reference Key
openalex_W7221080637 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Julia S Souza, Aline O. Albuquerque, Diego S Almeida, Andrielly H S Costa, Jean V Sampaio, João H M Silva
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag757
URL
https://doi.org/10.1093/bioinformatics/btag757
Keywords Keywords not found

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