Structures and scores for a benchmark comparing a deterministic collagen triple-helix builder (CDSM) against four co-folding models — Boltz-2, Chai-1, Protenix and AlphaFold3, the last in both with-MSA (af3_msa) and no-MSA (af3_nomsa) conditions — on 80 experimentally resolved collagen triple helices from the RCSB PDB.

Code: https://github.com/bm-howard/cdsm_benchmarking

Layout

Prefix	Contents	Size
`experimental/`	80 filtered experimental triple helices (`.cif`) + `manifest.csv/.parquet`	8.3 MB
`cdsm/<stage>/`	Deterministically built structures, one directory per pipeline stage	16 MB
`cdsm/<stage>/trajectories/`	MD trajectories (`.dcd` + starting `.pdb`) for the relaxed and annealed stages	95 MB
`predictions/<model>/`	`<PDB>_<model>.cif` for `boltz`, `chai`, `protenix`, `af3_msa`, `af3_nomsa`	19 MB
`scores/`	Scoring tables, as both `.csv` and `.parquet`	4.4 MB
`compute_cost/`	Runtime/cost benchmark records, seed-sweep scores, and the seed-sweep structures	15 MB

CDSM stages, in pipeline order: coreonly, fullseq, fullseq_reregistered, fullseq_reregistered_relaxed, fullseq_reregistered_annealed.

The prefixes are deliberate: reproducing the scoring needs experimental/, cdsm/ and predictions/ — about 40 MB — rather than the full 142 MB. The trajectories are the bulk of the dataset and are almost never needed.

Loading

The benchmark code resolves these paths for you:

from data_locations import experimental_cif_dir, predictions_dir, cdsm_dir

exp = experimental_cif_dir()                      # downloads on first use
af3 = predictions_dir("af3_msa")
mdt = cdsm_dir("fullseq_reregistered_relaxed", trajectories=True)

Directly, without the repo:

from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="CollagenHelixLabs/cdsm_benchmarking_data",
    repo_type="dataset",
    allow_patterns=["predictions/**", "experimental/**"],   # skip the trajectories
)

The four tabular configs load as datasets:

from datasets import load_dataset

scores = load_dataset("CollagenHelixLabs/cdsm_benchmarking_data", "scores_summary")

Schemas

manifest — one row per PDB entry (80 rows): pdb_id, kind (homotrimer | heterotrimer), n_distinct_chains, gly_start, frame_offset, has_hyp, len_a/b/c, chain_a/b/c_sequence. Sequences are one-letter codes with O = hydroxyproline (HYP).

scores_summary — one row per (structure, variant), 700 rows: pdb_id, variant, tm_score, global_rmsd_allatom, global_rmsd_backbone, global_lddt_allatom, global_lddt_backbone, coverage.

scores_per_residue — long format, 55,684 rows: pdb_id, variant, chain, resnum, rmsd_allatom, rmsd_backbone, lddt_allatom, lddt_backbone.

cdsm_stage_table — mean/median per CDSM pipeline stage and metric, written by figures/make_figures.py.

method_summary_table — mean/median per method and metric, formatted for the manuscript. A static snapshot, not a derived table: no script in the repository regenerates it, and it was built over a shared-target subset that differs from the current one, so its values will not match scores_summary exactly. Treat scores_summary as authoritative.

compute_cost/ — the compute-cost benchmark (CSV only, no parquet):

*_records*.csv — raw per-call records (wall seconds, peak VRAM, cold/warm phase, GPU, seed) for each method. *_records_seeds_shuffled*.csv are the randomised 5-seed sweeps on the 10 length-stratified targets; bare *_records.csv are the single-seed timing runs. The co-folder sweeps cover the 10-target subset (both L40S and L4 tiers); the CDSM records cover all 75 buildable targets (deterministic, single-threaded CPU, no seed).
seed_sensitivity_scores.csv — every seed-sweep structure scored against its experimental reference with the same machinery as scores_summary (one row per model × tier × seed × target).
seed_cifs/<model>[_L4]/seed_<n>/ — the structures themselves (<PDB>_<model>.cif). seed_cifs/af3/ holds locally generated AlphaFold 3 output (see Licensing). Each model's sweep is complete at 50 calls per tier (10 targets × 5 seeds), except protenix which has an L40S sweep only.
bench_manifest.json — the target/seed fingerprint every sweep runner asserts against; cdsm_machine.json — the CPU the CDSM records were timed on.

RMSD is computed in US-align's global-fit frame; lDDT is superposition-free. TM-score is Cα, reference-normalised (USalign -mm 1).

Coverage notes

All five prediction variants cover all 80 targets. The CDSM stages cover 75: five entries (1EI8, 6M80, 5K86, 7LXQ, 7LXP) have internal Gly-X-Y register interruptions that divide by zero in the builder's propensity step, so they have no deterministic build at any stage.
The annealed stage covers 19 structures, not 75 — it is a targeted comparison against the relaxed stage, not a full sweep.

Licensing

This dataset is mixed-licence. The prefixes are not interchangeable.

Prefix	Source	Terms
`experimental/`	RCSB PDB	CC0 — public domain
`cdsm/`	This work	See repository licence
`scores/`	This work	See repository licence
`compute_cost/` (except `seed_cifs/af3/`)	This work	See repository licence
`predictions/af3_msa/`, `predictions/af3_nomsa/`	AlphaFold Server (Google DeepMind)	Output Terms of Use — non-commercial only
`compute_cost/seed_cifs/af3/`	Local AlphaFold 3 run (Google DeepMind model)	Output Terms of Use — non-commercial only
`predictions/boltz/`, `chai/`, `protenix/`	Generated locally with the respective open models	Each model's own licence

predictions/af3_msa/ and predictions/af3_nomsa/ are AlphaFold Server Output. Google DeepMind's Output Terms of Use apply to it and to anything substantially derived from it, including a non-commercial restriction. Anyone redistributing or building on that prefix is bound by those terms; see https://alphafoldserver.com/terms. The scores in scores/ include variant == "af3_msa" and "af3_nomsa" rows derived from that Output. compute_cost/seed_cifs/af3/ is Output of a local AlphaFold 3 run; the same terms apply, and the model == "af3" rows in compute_cost/seed_sensitivity_scores.csv are derived from it.

Revision history

Structures are replaced in place rather than versioned, so changes that alter scores are recorded here. Superseded files remain in the dataset's commit history.

2026-09-01 — Added compute_cost/: the raw compute-cost benchmark records and the 5-seed sweep scores (seed_sensitivity_scores.csv, 10 length-stratified targets × 4 models × 5 seeds, L40S and L4 tiers), with the seed-sweep structures under seed_cifs/.
2026-08-14 — predictions/boltz/ regenerated from a local Boltz run; all 80 structures replaced. Median TM-score moved 0.913 → 0.914 and backbone RMSD 1.138 → 1.085 Å. scores/ was recomputed against the new structures.
2026-08-14 — AlphaFold3 prefixes renamed to name their MSA condition explicitly: af3 → af3_msa, AF3_no_MSA → af3_nomsa. Scores were relabelled, not recomputed; the af3_msa values are unchanged from the earlier af3 rows.

Citation

@unpublished{cdsm_collagen_benchmark,
  title  = {Deterministic and co-folding model predictions of collagen triple helices},
  author = {Howard, Bruno},
  note   = {Manuscript in preparation},
  year   = {2026}
}

Please also cite the RCSB PDB, US-align, and whichever prediction models you use.

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