---
license: cc-by-4.0
tags:
  - protein
  - structure
  - folding
  - co-folding
  - benchmark
---

# RepSP Benchmark

Everything needed to run the folding / co-folding / probing benchmarks
except per-encoder features (extract those from
[k-fold-structure/repsp-triprorep-tokens](https://huggingface.co/datasets/k-fold-structure/repsp-triprorep-tokens)
with your encoder, see the GitHub release README).

The benchmark goes **apo → holo**: predict properties of the bound
homodimer ("holo") from the unbound monomer ("apo"). The two chains are
identical (homodimer), so each residue position carries a single label.

Paper: [Atom-level Protein Representation Learning Improves Protein Structure Prediction](https://arxiv.org/abs/2605.22133) (arXiv:2605.22133).
GitHub release: [hsjang0/TriProRep](https://github.com/hsjang0/TriProRep).

## Contents

```
splits/
    folding/{train,valid,test}.txt        # 390,627 / 400 / 1,000 homodimer AFids
    probing/{train,valid,test}.txt        # 39,100 / 400 / 1,000 (10% of folding train)
probing/
    labels.pkl                            # per-residue labels: binding_site / delta_sasa_mean / levy_tier / bond_type_plip
REPSP_PDB/
    monomer/{valid,test}.tar.gz           # <AF-id>_monomer.pdb, chain A (apo)
    monomer/train_NNN.tar.gz              # sharded (~40 GB each)
    homodimer/{valid,test}.tar.gz         # <AF-id>.pdb, chain A + B (holo)
    homodimer/train_NNN.tar.gz            # sharded (~40 GB each)
boltz_apo_tokens/   shard*.tar            # Boltz-tokenized apo monomers (folding input)
boltz_apo_targets/  shard*.tar            # apo structure targets
boltz_holo_tokens/  shard*.tar            # Boltz-tokenized holo dimers (co-folding input)
boltz_holo_targets/ shard*.tar            # holo structure targets
```

The AFid in every filename is the same homodimer identifier taken from
`splits/{folding,probing}/{train,valid,test}.txt`. A monomer PDB is
resolved as `REPSP_PDB/monomer/<AF-id>_monomer.pdb`, the homodimer as
`REPSP_PDB/homodimer/<AF-id>.pdb`.

## Download

```bash
# Small assets (splits, labels, Boltz tokens)
hf download k-fold-structure/repsp-benchmark --repo-type dataset --local-dir ./benchmark \
    --exclude "REPSP_PDB/*"
cd ./benchmark/boltz_holo_tokens && for t in shard*.tar; do tar xf "$t"; done && rm shard*.tar

# Monomer PDBs, test split only (about 45 MB compressed)
hf download k-fold-structure/repsp-benchmark --repo-type dataset --local-dir ./benchmark \
    --include "REPSP_PDB/monomer/test.tar.gz"
mkdir -p ./REPSP_PDB/monomer
tar xzf ./benchmark/REPSP_PDB/monomer/test.tar.gz -C ./REPSP_PDB/monomer/

# For folding / co-folding training, grab train shards + the homodimer side.
```

## Notes

- Splits are LMDB-cleaned. A small number of AFids that fail Boltz
  tokenization are already dropped, so downstream stages won't hit
  KeyErrors.
- The `boltz_*` dirs use the tokenized layout the folding trunk
  (SimpleFold) reads: `manifest.json` + `records/` + `tokens/` (or
  `structures/` for targets). Point the folding datamodule's
  `tokenized_dir` / `target_dir` at these folders after extracting.

## License and attribution

- Code, splits, Boltz tokens, and probing labels: MIT.
- **Structures under `REPSP_PDB/`**: the homodimer PDBs are AFDB-Multimer
  predictions provided by NVIDIA to the
  [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/);
  the apo monomers are AlphaFold-2 single-chain predictions we generated.
  Both are redistributed here under
  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) with
  attribution to DeepMind and EMBL-EBI, per the AFDB
  [terms of use](https://alphafold.ebi.ac.uk/assets/Terms-of-Use.pdf).
  Cite Jumper et al., 2021 and Varadi et al., 2022, 2024 alongside the
  paper below.

## Citation

```bibtex
@misc{triprorep,
  title  = {Atom-level Protein Representation Learning Improves Protein Structure Prediction},
  author = {Kim, Taewon and Jang, Hyosoon and Seo, Hyunjin and Seo, Seonghwan and Kim, Hyeongwoo and Zhung, Wonho and Shin, Mingyeong and Kim, Wooyoun and Ahn, Sungsoo},
  year   = {2026},
  eprint = {2605.22133},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url    = {https://arxiv.org/abs/2605.22133}
}
```