--- pretty_name: "BILBY-PRO: PROSITE-derived functional motif scaffolding benchmark" license: cc-by-4.0 task_categories: - other tags: - biology - protein-design - protein-structure - motif-scaffolding - benchmark - prosite size_categories: - n<1K --- # 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](https://github.com/PROFOUND-HFMI/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: *Static Recovery Is Not Dynamic Stability: Dynamics-Aware Benchmarking of Protein Motif Scaffolding*, by Emile de Bruyn, Moritz Schäffler, Jannik Schneider, Sebastian R. Schmidt, Chanwoong Hwang, Aleena Siji, Utkarsh Upadhyay, Alexander Schug, Stefan Bauer, Artur Yakimovich, Stefan Kesselheim and Alina Bazarova. See [CITATION.cff](CITATION.cff). The first three authors contributed equally. ## Quick start Once published, 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: ```bash 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](croissant.json) for machine-readable table fields, links between tables and structure collections. For example, after `pip install mlcroissant`, read a local case with: ```python 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")))) ``` ## 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](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](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](https://prosite.expasy.org/). 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](https://prosite.expasy.org/prosite_license.html). 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](provenance/README.md). ## MotifBench quick start The following creates a separate BILBY-PRO working copy of the tested [MotifBench](https://github.com/blt2114/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. ```bash 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](https://github.com/RosettaCommons/RFdiffusion) and its model weights, a possible invocation for the first case is: ```bash 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/$CASE" \ 'contigmap.contigs=[0-185/A1-42/0-185]' \ 'contigmap.length=225-225' inference.num_designs=100 ``` This generation example is **not tested here**; consult the generator's documentation and honor the redesignable-residue constraints during sequence design. Contigs are ranges, not literal placements. Each case directory must contain `{case}_{sample_num}.pdb` files with 1-indexed residues and a `scaffold_info.csv` with `sample_num,motif_placements`, e.g. `0,50/A/133` for a 225-residue scaffold containing the 42-residue segment A. Use each design's **actual** placement, not the example. For multiple segments use their actual chain order, e.g. `12/B/15/A/29`. Upstream's `example/write_scaffold_info.py` is usable only if the generator's B-factors actually distinguish motif from scaffold; inspect its output. To evaluate, prepare the Foldseek PDB database as described in the upstream README, then create a configuration (these paths must exist): ```bash export RESULTS="$PWD/evaluation" export FOLDSEEK_DB=/path/to/foldseek/database/pdb mkdir -p "$RESULTS" cat > "$WORK/config-bilby.txt" <