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DeepRank-Ab: A scoring function for antibody-antigen complexes based on geometric deep learning
1. DeepRank-Ab targets a key failure mode in antibody–antigen modeling: good poses are often sampled but not ranked correctly. The paper shows an example where AlphaFold3 generates a DockQ 0.6 model yet ranks it 498th, illustrating that scoring (not only sampling) is a central bottleneck.
2. The authors introduce a rigorously curated benchmark tailored to antibody–antigen interfaces: ~2.3 million decoys from 1,442 complexes (from SAbDab), generated under four HADDOCK3 protocols (bound/unbound docking and refinement). Complexes are clustered with Foldseek-Multimer and split at the cluster level to reduce data leakage.
3. DeepRank-Ab is trained as a regression model to predict continuous DockQ (pDockQ), rather than a binary “acceptable/not acceptable” label. This preserves the full spectrum of model quality and directly supports ranking.
4. A major design finding: atom-level interface graphs outperform residue-level graphs for antibody–antigen scoring, consistent with the importance of fine-grained CDR loop geometry. The best-performing formulations combine atom-level nodes with interface-focused edges.
5. The interface representation is strengthened using Voronoi-based surface decomposition: edges can encode either simple interatomic distances or Voronoi contact areas. Across cross-validation, Voronoi contact area provides a consistent edge over distance-only encoding for predicting and classifying near-native poses.
6. Antibody-specific and physics-inspired features matter. Adding IMGT-derived region labels (e.g., CDR annotations) and geometric/energetic descriptors improves performance and stability; ablations show large error increases when removing region labels, atom type, covalent interactions, orientation, or Voronoi contact area. In contrast, raw ESM-2 embeddings are partly redundant, and antibody-specific fine-tuning (AbTune) yields only marginal gains relative to added complexity.
7. Training data sampling is treated as a first-class modeling choice. Besides “balanced” sampling across DockQ bins, they test targeted upsampling of low-quality decoys (DockQ < 0.23) to increase structural diversity in failure modes; this slightly improves overall learning and robustness.
8. Architecture: an E(n)-equivariant GNN with a two-branch design that processes interface edges and internal edges separately, then pools with global attention and predicts pDockQ. This keeps rotational/translational symmetry while learning local interface geometry.
9. On the hardest docking benchmark setting (unbound–unbound docking; n=215), DeepRank-Ab variants outperform HADDOCK EMscoring and VoroIF-jury in Top-k success. Atom-distance edges do best at Top1, while Voronoi-area edges become strongest at higher k (e.g., Top10), reflecting tradeoffs between sharp early ranking and broader enrichment.
10. Generalization beyond docking decoys: on an AF3-focused external test set (59 complexes released after AF3’s training cutoff), DeepRank-Ab improves AF3 Top1 success by 35.5% and more than doubles mean Top1 DockQ. A notable practical tweak is removing the electrostatics term to better handle unrelaxed AF3 structures (distribution shift from clashes/packing), boosting Top1 success to 54.24%.
11. External CAPRI/MassiveFold evaluation (5 antibody–antigen targets) suggests strong out-of-distribution performance: DeepRank-Ab reaches 100% Top5 success, with especially strong results on peptide antigens. The paper also analyzes why: peptide interfaces tend to be more locally driven and compatible with the model’s 5 Å heavy-atom cutoff graph construction.
12. The work argues that “consensus scoring” is not automatically beneficial: combining DeepRank-Ab with AF3 via averaging or jury voting reduces performance, implying DeepRank-Ab already captures much of the useful ranking signal for these systems.
💻Code: https://t.co/qfHjbNkCGs
📜Paper: https://t.co/RWdeEu450N
#ComputationalBiology #StructuralBioinformatics #Antibodies #ProteinDocking #GeometricDeepLearning #GNN #AlphaFold3 #HADDOCK #CAPRI #BenchmarkDatasets
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