Graph neural networks
Can predicted protein shapes help find where antibodies bind?
Open access · cc by · source: Europe PMC
Feeding AlphaFold2-predicted 3D structures into a graph neural network made antibody-binding-site prediction clearly better than sequence-only and earlier structure-based tools.
Study at a glance
- Design
- Computational / modelling — Supervised residue-level classifier trained on antigen chains from the Protein Data Bank; 10-fold cross-validation plus a time-split independent test set of antigens deposited after January 2021.
- N
- N=633 · 633 non-redundant antigen sequences: 577 for training and 56 for the independent test (the test set holds 1393 epitope residues).
- Population
- Antigen protein chains from antibody-antigen complexes in the Protein Data Bank
- Outcome
- Per-residue epitope prediction scored by AUC, AUPR, F1 and MCC
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Key findings
On the independent test GraphBepi reached an AUC of 0.751 and AUPR of 0.261, beating the next-best tool (ScanNet with transfer learning) by 44.0% in AUPR. Removing the graph module caused the biggest drop, and the advantage over a transformer baseline grew for residues with many long-range contacts. Using real experimental structures instead of AlphaFold2 predictions barely changed results, but antigens with poorly predicted structures got much worse F1 (0.241 versus 0.406 for the best-predicted fifth).
Methodology
The authors built GraphBepi, which takes only an antigen's amino-acid sequence, predicts its 3D shape with AlphaFold2, and turns that shape into a graph of residues linked by spatial and sequence neighbours. An edge-enhanced graph neural network reads the graph while a bidirectional LSTM reads the sequence (represented by the ESM-2 protein language model); the two are combined to score each residue as epitope or not. They trained on 577 antigens and tested on 56 later-deposited antigens, comparing against two sequence-based and four structure-based published tools and running ablations.
Limitations
Absolute performance is still modest: an AUPR around a quarter means most predicted epitope residues are wrong, so the tool narrows the search rather than replacing experiments. The test set is small (56 antigens) and competitors were run with default settings rather than retrained on the same data. The authors note the model depends on AlphaFold2 quality and is a black box, and features were chosen using cross-validation results, which can slightly flatter the final design.
How this study connects
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