BILBY-PRO

BILBY-PRO is a benchmark of structurally conserved functional motifs derived from PROSITE-linked experimental protein structures. It provides fixed motif-scaffolding objectives for comparing protein design methods. It is the benchmark construction and static dataset. The related BILBY-MD molecular dynamics and dynamics-aware evaluation pipeline is maintained separately.

It contains 220 cases spanning 174 PROSITE patterns. Construction software was developed by Emile de Bruyn within PROFOUND, a pilot project of the Helmholtz Foundation Model Initiative (HFMI) of the Helmholtz Association.

Associated publication:

de Bruyn et al., Static Recovery Is Not Dynamic Stability: Dynamics-Aware Benchmarking of Protein Motif Scaffolding, accepted at NeurIPS 2026 (Evaluations & Datasets Track).

• Paper DOI: https://doi.org/10.48550/arXiv.2611.07723
• Dataset DOI: https://doi.org/10.57967/hf/11443

The first three authors contributed equally. See CITATION.cff for the complete author list and preferred citation.

Quick start

Download with hf download HFMI-PROFOUND/BILBY-PRO --repo-type dataset --local-dir BILBY-PRO or clone the dataset repository with Git LFS. From this checkout:

python3 scripts/validate_release.py

The validator checks case and pattern counts, table relationships, residue coordinates, contig feasibility and checksums, including regression against the historical submission. See croissant.json for machine-readable table fields, links between tables and structure collections, and for the Responsible-AI and provenance metadata (Croissant 1.1 and http://mlcommons.org/croissant/RAI/1.0, rai:* and prov:* properties). It is regenerated by python3 scripts/build_croissant.py and passes mlcroissant validate --jsonld croissant.json without errors. For example, after pip install mlcroissant, read a local case with:

import mlcroissant as mlc

dataset = mlc.Dataset("croissant.json", mapping={"test_cases.csv": "test_cases.csv"})
print(next(iter(dataset.records(record_set="test_cases"))))

Release integrity and citation

The current citable BILBY-PRO dataset release has DOI 10.57967/hf/11443, corresponding to revision c8f857d. Its underlying benchmark data are checksum-pinned in provenance/release-sha256.txt.

provenance/huggingface-manifest.json records every hosted resolve/main/<path> URL with its exact content type, size and LFS marker: 450 entries, namely the two structure directories, the five CSV tables, the hosted README.md, LICENSE and .gitattributes. The 440 PDB files are LFS objects; the CSV and JSON files are plain Git objects, so Git LFS filter rules cannot be used as an integrity check for them. provenance/published-sha256.txt retains the historical digests.

Dataset contents

File	Contents
test_cases.csv	Case IDs, normalized motif residues, redesignable residues, scaffold length and group
motif_cases_metadata_croissant.csv	PROSITE accessions and patterns, structural annotations, original and normalized residue labels, and PDB paths
contig_specifications.csv	Normalized and original contigs and target lengths
contigs.csv	Original-numbering contigs
cluster_membership.csv	Cluster membership, including non-representative members
motif_pdbs/	One normalized motif PDB per case; one chain per motif segment
original_pdbs/	Corresponding source structures

Cluster case_id values use the same _cN identifiers as the case metadata. Member rows can name a different PROSITE entry within the representative's cluster; join to cases using benchmark_pdb_id.

Group 4 denotes multi-state motifs, not a particular segment count; groups 1–3 follow the published segment-based grouping. Redesignable positions in the published motif PDBs retain their original residue identities: use the MotifBench adapter below to encode them as UNK for evaluation.

This corrected release adjusts one infeasible contig and standardizes cluster case IDs. Historical paper results were not recomputed; see CHANGELOG.md for the exact changes.

Construction and provenance

The construction code parses PROSITE sequence patterns and associated PDB identifiers, extracts structural motif instances, analyzes structural conservation using RMSD and Foldseek clustering, selects representatives, and exports motif structures, case tables and normalized contigs. See construction/README.md for the audited snapshot, inputs, entry point and limitations.

The benchmark used PROSITE 2021_04, dated 29 September 2021. Its 3D field supplied the PDB cross-references used to select structures; that field is absent from the current PROSITE data file. The wrapper verifies input hashes. The historical code revision, PDB snapshot and software environment are not fully established, so a fresh reconstruction has not been validated.

PROSITE is maintained by the SIB Swiss Institute of Bioinformatics. No PROSITE database archive, complete database entry or PROSITE documentation file is distributed here. The published metadata does contain verbatim PROSITE sequence-pattern strings, alongside accessions and links; these fields have not been removed. The PROSITE 2021_04 input identifies its license as CC BY-NC-ND 4.0; see PROSITE's licensing conditions. That license and PDB terms apply to upstream material; the dataset's CC BY notice does not grant rights to the pattern strings or PDB structures. See provenance/README.md.

MotifBench quick start

The following creates a separate BILBY-PRO working copy of the tested MotifBench revision. It does not overwrite the original checkout or the published PDBs. Use absolute paths without spaces: upstream shell scripts do not consistently quote paths.

export BILBY="$PWD"
git clone https://github.com/blt2114/MotifBench.git ../MotifBench
git -C ../MotifBench checkout d5a714c6a376c701e29dfa66d42a1b13c5e339e0
python3 scripts/prepare_motifbench.py ../MotifBench ../MotifBench-BILBY
export WORK="$(cd ../MotifBench-BILBY && pwd)"

# Upstream evaluation environment; this can take substantial time and space.
conda env create -f "$WORK/motif_bench.yml"
conda activate motif_bench
pip install -e "$WORK/Scaffold-Lab"

The helper prefixes IDs with their one-based CSV row number (e.g. 01_1CK7_PS00023_c1), writes a separate test_cases.csv, adapts PDBs by marking redesignable residues UNK and retaining only backbone atoms there, and writes compatible normalized contigs. All other motif coordinates are unchanged. The source checkout must be clean and at the pinned commit.

Generate scaffolds with a separately installed MotifBench-compatible method. For example, after installing RFdiffusion and its model weights, a possible invocation for the first case is:

export CASE=01_1CK7_PS00023_c1
export SCAFFOLDS="$PWD/scaffolds"
mkdir -p "$SCAFFOLDS/$CASE"
python /path/to/RFdiffusion/scripts/run_inference.py \
  "inference.input_pdb=$BILBY/motif_pdbs/1CK7_PS00023_c1.pdb" \
  "inference.output_prefix=$SCAFFOLDS/$CASE/$CASE" \
  'contigmap.contigs=[0-185/A1-42/0-185]' \
  'contigmap.length=225-225' inference.num_designs=100

This generation example is not tested here; consult the generator's documentation and honor the redesignable-residue constraints during sequence design. Contigs are ranges, not literal placements. Each case directory must contain {case}_{sample_num}.pdb files with 1-indexed residues and a scaffold_info.csv with sample_num,motif_placements, e.g. 0,50/A/133 for a 225-residue scaffold containing the 42-residue segment A. Use each design's actual placement, not the example. For multiple segments use their actual chain order, e.g. 12/B/15/A/29. Upstream's example/write_scaffold_info.py is usable only if the generator's B-factors actually distinguish motif from scaffold; inspect its output.

To evaluate, prepare the Foldseek PDB database as described in the upstream README, then create a configuration (these paths must exist):

export RESULTS="$PWD/evaluation"
export FOLDSEEK_DB=/path/to/foldseek/database/pdb
mkdir -p "$RESULTS"
cat > "$WORK/config-bilby.txt" <<EOF
scaffold_base_dir=$SCAFFOLDS
benchmark_dir=$WORK
foldseek_db_path=$FOLDSEEK_DB
base_output_dir=$RESULTS
python_path=$(command -v python)
EOF
cd "$WORK"
./scripts/evaluate_bbs.sh "$CASE" config-bilby.txt
# After evaluation of all intended cases:
./scripts/summarize_results.sh config-bilby.txt
python "$BILBY/scripts/summarize_motifbench.py" "$WORK" "$RESULTS"

The last command preserves the upstream per-problem summary, orders cases before applying upstream bootstrap scoring, and emits ordered per-case scores and an overall summary with a fixed random seed. It requires 100 evaluated designs per included case; a subset is explicitly labeled partial. Upstream's overall group metrics average group means rather than weighting by case count. The interface and summary scripts were smoke-tested locally; scaffold generation, refolding, Foldseek database search and full GPU evaluation were not run. See provenance/README.md for validation.

Attribution and citation

Please cite the dataset release itself as described in CITATION.cff: BILBY-PRO: PROSITE-derived functional motif scaffolding benchmark, DOI 10.57967/hf/11443, and the associated publication Static Recovery Is Not Dynamic Stability: Dynamics-Aware Benchmarking of Protein Motif Scaffolding. The machine-readable preferred citation is the citeAs field of croissant.json.

Benchmark case definitions and added annotations are CC BY 4.0. Construction code and release scripts are Apache-2.0, copyright 2026 Emile de Bruyn. PDB structures and PROSITE material retain their upstream terms. See CITATION.cff for the publication's full author list.

Please also cite:

• Sigrist et al., “New and continuing developments at PROSITE,” Nucleic Acids Research (2013), doi:10.1093/nar/gks1067.
• MotifBench, arXiv:2502.12479.
• The source PDB structures and their corresponding experimental publications.