AutoHMD: scalable and accurate validation of antibody-antigen complexes by heated molecular dynamics.
4 affiliations
- 1Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Ceará, Av. Humberto Monte s/n, Fortaleza, Ceará, 60020-181, Brazil.
- 2Structural and Functional Biology in Biopharmaceuticals Group, Fiocruz Ceará, R. São José, s/n, Eusébio, Ceará, 61773-270, Brazil.
- 3Postgraduate Program in Computational and Systems Biology, Oswaldo Cruz Foundation (Fiocruz), Av. Brasil, 4365, Rio de Janeiro, Rio de Janeiro, 21040-900, Brazil.
- 4Pasteur-Fiocruz Center on Immunology and Immunotherapy, Oswaldo Cruz Foundation (Fiocruz), R. São José, S/N, Eusébio, Ceará, 61773-270, Brazil.
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.
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BibTeX
@article{souza2026autohmd,
title = {{AutoHMD: scalable and accurate validation of antibody-antigen complexes by heated molecular dynamics.}},
author = {Julia S Souza and Aline O Albuquerque and Diego S Almeida and Andrielly H S Costa and Jean V Sampaio and João H M Silva},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag757},
url = {https://europepmc.org/article/MED/42853673},
note = {CC BY},
}
RIS
TY - JOUR
TI - AutoHMD: scalable and accurate validation of antibody-antigen complexes by heated molecular dynamics.
AU - Julia S Souza
AU - Aline O Albuquerque
AU - Diego S Almeida
AU - Andrielly H S Costa
AU - Jean V Sampaio
AU - João H M Silva
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag757
UR - https://europepmc.org/article/MED/42853673
AB - 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.
SN - 1367-4803
SN - 1367-4811
ER -