README.md

# Antibody Inverse Folding Benchmark

## Dataset

512 heavy + 512 light antibody variable domain structures sampled from the **test split** of the structure AntiFold [1] and AbMPNN [2] structure dataset.

### Selection procedure

1. For every sequence in the test split, fold the native sequence with IgFold [3] and compute CDR3 RMSD against the reference PDB (framework-aligned, Cα only).
2. Keep only structures where CDR3 RMSD < 1 Å — this ensures IgFold can faithfully represent the structure, eliminating evaluation bias from IgFold's own limitations.
3. Randomly sample 512 heavy and 512 light chains from the filtered set (seed 42).

## Files

```
if_bench_combined.csv   — task table (1024 rows)
pdbs/
  oas/                     — OAS-derived structures
  sabdab/                  — SAbDab-derived structures
```

### Table columns

| Column | Description |
|---|---|
| `chain_type` | `heavy` or `light` |
| `pdb_local` | path to PDB relative to this directory, e.g. `pdbs/oas/di9q.pdb` |
| `chain_id` | chain identifier in the PDB file |
| `global_index` | index in the original dataset |
| `residue_start` | start index for variable domain extraction (inclusive); `NaN` = use full chain |
| `residue_end` | end index for variable domain extraction (exclusive); `NaN` = use full chain |
| `native_seq` | native variable domain amino acid sequence |

## Metrics

Each model is run once per structure. Metrics are computed as follows.

### AAR (Amino Acid Recovery)

Fraction of positions where the generated sequence matches the native sequence, reported per IMGT region: FR1, CDR1, FR2, CDR2, FR3, CDR3, and globally.

### Global RMSD and CDR3 RMSD

For each generated sequence:

1. Fold with IgFold → Cα coordinates of the generated structure.
2. Extract Cα from the reference PDB (variable domain chain).
3. IMGT-number both sequences → find shared positions.
4. **Global RMSD**: Kabsch-align on all shared Cα → RMSD over all shared positions.
5. **CDR3 RMSD**: Kabsch-align on framework Cα only → RMSD over CDR3 positions (IMGT 105–117).


# Links
[1] Magnus Haraldson Høie, Alissa M Hummer, Tobias H Olsen, Broncio Aguilar-Sanjuan, Morten Nielsen, Charlotte M Deane, AntiFold: improved structure-based antibody design using inverse folding, Bioinformatics Advances, Volume 5, Issue 1, 2025, vbae202, https://doi.org/10.1093/bioadv/vbae202
[2] Frédéric A Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M Deane, Inverse folding for antibody sequence design using deep learning, arXiv preprint, 2023, arXiv:2310.19513, https://arxiv.org/abs/2310.19513
[3] Ruffolo, J.A., Chu, LS., Mahajan, S.P. et al. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun 14, 2389 (2023). https://doi.org/10.1038/s41467-023-38063-x