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Related Concept Videos

Antibody Structure01:10

Antibody Structure

Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...

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  1. Home
  3. Deeprank-ab: A Scoring Function For Antibody-antigen Complexes Based On Geometric Deep Learning.
  1. Home
  3. Deeprank-ab: A Scoring Function For Antibody-antigen Complexes Based On Geometric Deep Learning.

Related Experiment Video

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.

Xiaotong Xu1, Ilaria Coratella1, Victor Reys1

  • 1Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, CH Utrecht, The Netherlands.

Communications Biology
|June 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

DeepRank-Ab, a new deep learning scoring function, significantly improves the accuracy of modeling antibody-antigen interactions. It outperforms existing methods like AlphaFold3 in identifying correct complex structures.

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Area of Science:

  • Structural biology
  • Immunology
  • Computational chemistry

Background:

  • Accurate modeling of antibody-antigen interactions is crucial for understanding immune responses and designing therapeutics.
  • Current computational methods, including AI-based approaches like AlphaFold3, face challenges in generating and ranking near-native conformations of these complexes.

Purpose of the Study:

  • To develop a novel, highly accurate scoring function for antibody-antigen complex modeling.
  • To address the limitations of existing methods in predicting the structural accuracy of antibody-antigen interfaces.

Main Methods:

  • Developed DeepRank-Ab, a geometric deep learning-based scoring function specifically designed for antibody-antigen interfaces.
  • Created a large, diverse benchmark dataset of approximately 2.3 million decoys from 1,442 complexes for training and evaluation.
  • Systematically evaluated various graph representations, structural/energetic features, and sampling strategies, identifying atom-level representations with Voronoi-based surface decomposition and antibody-specific features as optimal.

    • Main Results:

    • DeepRank-Ab consistently outperformed AlphaFold3, HADDOCK, and other state-of-the-art scoring functions across multiple independent test sets.
    • Achieved a 35.5% increase in AlphaFold3's Top1 success rate and more than doubled the mean Top1 DockQ score.
    • Demonstrated strong generalization capabilities, achieving a 100% Top5 success rate on external antibody-antigen CAPRI targets.

    Conclusions:

    • DeepRank-Ab represents a significant advancement in scoring antibody-antigen complex models.
    • The method substantially improves the identification of near-native antibody-antigen conformations, offering a powerful tool for structural biology and drug discovery.