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<p>Accurate antibody-antigen (Ab-Ag) docking is hindered by CDR flexibility, discontinuous epitopes, and the absence of reliable binding-site restraints. This study presents a deep learning-augmented docking framework that integrates ParaDeep, a sequence-based paratope predictor, with the PyDockWEB scoring engine to provides a practical and interpretable framework for guiding docking using sequence-derived paratopes. ParaDeep predicts binding residues directly from concatenated VH/VL sequences, and these residues are used as spatial restraints within the PyDockWEB pipeline. Across 50 Ab-Ag complexes from AACDB, DL-guided targeted docking improved performance for the majority of cases relative to blind docking. Interface RMSD decreased overall (median 10.171 Å to 1.193 Å, <i>p</i> = 0.0016), and TM-score proximity showed a significant shift toward native folds (<i>p</i> = 0.0256). DockQ distributions exhibited a clear rightward shift, with median scores increasing from 0.0523 to 0.6799 and 46% of targeted models reaching high-quality classification. Structural analysis indicated that high-DockQ interfaces were more hydrophilic (–1.33 ± 0.52 vs. − 0.56 ± 0.88, <i>p</i> = 0.037) and enriched in coil regions, suggesting that moderate flexibility and polar complementarity may be associated with near-native docking convergence. Cross-metric analysis evaluated strong agreement between TM-score and DockQ (ρ = 0.854 for targeted vs. 0.782 for blind), indicating concurrent improvements in interface and global accuracy. Importantly, paratope-size correlation analyses showed no association with docking accuracy, whereas re-analysis of initially misclassified models using AppA-derived paratopes recovered most models, suggesting that the spatial precision of predicted restraint placement is a major contributor to docking outcomes in this rigid-body setting, while restraint count alone is not informative. In summary, ParaDeep-guided docking provides a practical and interpretable framework for integrating DL-derived paratope information into a physics-based docking framework. Rather than introducing a new docking paradigm, this work suggests that DL-derived residue-level priors can improve the efficiency and accuracy of rigid-body Ab-Ag docking on average, while retaining physical transparency and mechanistic interpretability. The framework offers a scalable and biologically informed complement to blind docking, with potential for integration into iterative antibody design and structure-guided immune-engineering workflows.</p>

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Integrating deep learning with physics based modeling enables high precision antibody antigen interface prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Kanchanok Kodchakorn,
  • Piyachat Udomwong,
  • Thanathat Pamonsupornwichit,
  • Thanyaluck Phitak,
  • Chatchai Tayapiwatana

摘要

Accurate antibody-antigen (Ab-Ag) docking is hindered by CDR flexibility, discontinuous epitopes, and the absence of reliable binding-site restraints. This study presents a deep learning-augmented docking framework that integrates ParaDeep, a sequence-based paratope predictor, with the PyDockWEB scoring engine to provides a practical and interpretable framework for guiding docking using sequence-derived paratopes. ParaDeep predicts binding residues directly from concatenated VH/VL sequences, and these residues are used as spatial restraints within the PyDockWEB pipeline. Across 50 Ab-Ag complexes from AACDB, DL-guided targeted docking improved performance for the majority of cases relative to blind docking. Interface RMSD decreased overall (median 10.171 Å to 1.193 Å, p = 0.0016), and TM-score proximity showed a significant shift toward native folds (p = 0.0256). DockQ distributions exhibited a clear rightward shift, with median scores increasing from 0.0523 to 0.6799 and 46% of targeted models reaching high-quality classification. Structural analysis indicated that high-DockQ interfaces were more hydrophilic (–1.33 ± 0.52 vs. − 0.56 ± 0.88, p = 0.037) and enriched in coil regions, suggesting that moderate flexibility and polar complementarity may be associated with near-native docking convergence. Cross-metric analysis evaluated strong agreement between TM-score and DockQ (ρ = 0.854 for targeted vs. 0.782 for blind), indicating concurrent improvements in interface and global accuracy. Importantly, paratope-size correlation analyses showed no association with docking accuracy, whereas re-analysis of initially misclassified models using AppA-derived paratopes recovered most models, suggesting that the spatial precision of predicted restraint placement is a major contributor to docking outcomes in this rigid-body setting, while restraint count alone is not informative. In summary, ParaDeep-guided docking provides a practical and interpretable framework for integrating DL-derived paratope information into a physics-based docking framework. Rather than introducing a new docking paradigm, this work suggests that DL-derived residue-level priors can improve the efficiency and accuracy of rigid-body Ab-Ag docking on average, while retaining physical transparency and mechanistic interpretability. The framework offers a scalable and biologically informed complement to blind docking, with potential for integration into iterative antibody design and structure-guided immune-engineering workflows.