Bindcraft

BindCraft — hallucination-based de novo binder design

What this is

BindCraft is an endless trajectory loop that designs de novo binders for a target protein and stops only when it has the requested number of designs passing every filter (or hits max_trajectories, or the auto-acceptance-rate monitor decides nothing is working). It is the hallucination camp's reference pipeline — the inverse of diffusion tools like RFdiffusion3 / BoltzGen / Genie 3 / DISCO.

The pipeline per trajectory is fixed:

Step	What runs	Output dir
1. Sample	random length ∈ [min, max], random seed, sample helicity from the prior	—
2. Hallucinate	AF2 backprop with the design_algorithm (2stage / 3stage / 4stage / greedy / mcmc)	Trajectory/
3. Triage	trajectory tagged as low-confidence, clashing, or kept	Trajectory/LowConfidence · Trajectory/Clashing · Trajectory/Relaxed
4. Relax	PyRosetta FastRelax of the AF2 trajectory PDB	Trajectory/Relaxed/
5. Score	PyRosetta interface metrics (dG, dSASA, SC, PackStat, H-bonds, unsat-Hbonds, hydrophobicity, secondary structure, clashes) + RMSDs	row in trajectory_stats.csv
6. MPNN redesign	ProteinMPNN (or SolubleMPNN) generates num_seqs sequences for the binder chain (interface optionally fixed)	—
7. AF2 repredict	every passing MPNN sequence is re-predicted as a complex (5 AF2 models) and as a binder monomer	MPNN/, MPNN/Relaxed/, MPNN/Binder/
8. Filter	per-model + average filters checked against the filter JSON; H-bond, dG, SC, ipTM, pLDDT, etc.	Accepted/ or Rejected/
9. Loop	if accepted_designs ≥ number_of_final_designs → rank Accepted PDBs and stop; otherwise next trajectory	—

The whole thing is steered by three JSON files:

--settings  ./settings_target/<your_target>.json      # required: PDB, chains, hotspot, lengths
--filters   ./settings_filters/<preset>.json          # default: default_filters.json
--advanced  ./settings_advanced/<preset>.json         # default: default_4stage_multimer.json

--filters and --advanced are optional; omitting them falls back to the defaults above. The script writes everything under target_settings["design_path"].

Top rule of thumb (from the upstream README): Always trim the input target PDB to the smallest size possible. GPU memory blows up with target size, and bigger targets mean more deformed-binder trajectories. Strip everything outside the targeted chains / patch / domain.

When to use BindCraft vs. alternatives

You want…	Use
De novo mini-protein binder against a protein, hallucination-based, fully AF2-validated, with a battery of biophysical filters	BindCraft
Diffusion-based all-atom binder against protein / peptide / small-molecule / DNA / RNA, with ipSAE / affinity / refold filters	boltzgen
Backbone-only diffusion (then plug your own MPNN + validation)	foundry (RFdiffusion3) / genie3
Sequence + structure co-design conditioned on a small molecule / metal / DNA / RNA	disco
Peptide binder specifically, with helical / cyclic / disulfide / stapled / cyclotide forms	boltzgen (peptide-anything) — BindCraft can do linear peptides via the peptide_3stage_multimer preset but not cyclic / stapled
Antibody / nanobody design	boltzgen (antibody-anything / nanobody-anything) — BindCraft is not antibody-aware
Small-molecule binder	boltzgen (protein-small_molecule) — BindCraft has no ligand mode
Validate accepted designs with an independent structure predictor	chai-lab / boltz / protenix / esm-biohub (ESMFold2) / fair-esm (ESMFold)
Cluster / score the accepted set	biotite (parse, compute metrics) + foldseek (external)
Wrap the campaign on SLURM with per-target arrays	protflow

Installation (one-shot)

git clone https://github.com/martinpacesa/BindCraft  /path/to/bindcraft
cd /path/to/bindcraft
bash install_bindcraft.sh --cuda '12.4' --pkg_manager 'conda'   # or 'mamba'

That single script creates a BindCraft conda env (Python 3.10, JAX + CUDA, NumPy < 2, Biopython, PyRosetta, ColabDesign), then downloads and extracts the AF2 weights (~5.3 GB) into ./params/ inside the install directory. After it finishes:

conda activate BindCraft
python -c "import colabdesign, pyrosetta; print('ok')"

Requirements:

• CUDA-capable Nvidia GPU (no CPU fallback for the AF2 step).
• ≥ 32 GB GPU memory recommended for larger target+binder complexes.
• ~ 2 MB code + ~ 5.3 GB AF2 weights on disk.
• PyRosetta license is required for commercial use (gratis for non-commercial / academic).

Full details, GPU sizing, and per-machine setups → references/installation.md.

The three input JSONs

settings_target/*.json — what to design

{
  "design_path":              "/abs/path/to/output/dir/",
  "binder_name":              "PDL1",
  "starting_pdb":             "/abs/path/to/target.pdb",
  "chains":                   "A",
  "target_hotspot_residues":  "56",
  "lengths":                  [65, 150],
  "number_of_final_designs":  100
}

target_hotspot_residues syntax: null (let AF2 pick the site), a single residue ("56"), a range ("1-20"), comma-separated mix ("1,2-10"), chain-prefixed ("A1-10,B1-20"), or whole chains ("A"). When in doubt, pick a small patch — it dramatically reduces search space.

lengths is a [min, max] range; each trajectory samples uniformly.

settings_filters/*.json — what counts as a hit

Five built-in presets:

• default_filters.json — tight, recommended starting point.
• relaxed_filters.json — loosen i_pTM / i_pAE / ShapeComplementarity / unsat-Hbonds / Hotspot_RMSD for difficult targets.
• peptide_filters.json — tuned for peptide binders (lower i_pTM bar, looser Hotspot_RMSD).
• peptide_relaxed_filters.json — peptide + relaxed.
• no_filters.json — every threshold null → see what AF2 produces.

Each filter row is { "threshold": <number|null>, "higher": <bool> }. Set threshold: null to disable that filter. Each metric is checked per AF2 model (1_<metric>…5_<metric>) and as an Average_<metric> — disabling individual models lets you require, say, only model 1 + model 2 to pass.

Full list of metrics and recommended ranges → references/filters.md.

settings_advanced/*.json — how to design

Combine one base flavor with optional modifiers:

Base	Use when	weights_helicity	design_algorithm	force_reject_AA
default_4stage_multimer	typical alpha-helix-biased mini-protein binder	-0.3	4stage	false
betasheet_4stage_multimer	binder should be β-sheet rich (e.g. β-barrel scaffold target)	-2.0 (strong β bias)	4stage	false
peptide_3stage_multimer	short peptide binders, fully helical bias	+0.95	3stage	true (block C)

Modifiers (composable, e.g. default_4stage_multimer_mpnn_flexible_hardtarget):

Suffix	Changes vs base	When to use
_mpnn	mpnn_fix_interface=false → MPNN can redesign interface residues	When the hallucinated interface is suboptimal and you want MPNN free to re-route
_flexible	rm_template_seq_design=true, rm_template_seq_predict=true → mask target sequence in template	When the target backbone should be allowed to move (cryptic pockets, flexible loops)
_hardtarget	predict_initial_guess=true → warm-start AF2 reprediction from binder atom positions	When designs are good in trajectory but fail reprediction (rigid targets where AF2 reprediction "forgets" the binder)

All 20+ shipped presets are described in references/advanced-settings.md.

Run it

Local (no SLURM)

conda activate BindCraft
cd /path/to/bindcraft
python -u ./bindcraft.py \
    --settings  ./settings_target/PDL1.json \
    --filters   ./settings_filters/default_filters.json \
    --advanced  ./settings_advanced/default_4stage_multimer.json

SLURM

sbatch ./bindcraft.slurm \
    --settings  ./settings_target/PDL1.json \
    --filters   ./settings_filters/default_filters.json \
    --advanced  ./settings_advanced/default_4stage_multimer.json

The shipped SLURM script asks for 1 GPU, 42 GB RAM, 72 h walltime, and a bindcraft_<jobid>.log log file. Adjust the #SBATCH header for your cluster.

The script is resumable — re-running with the same design_path continues from where it left off. Existing trajectory PDBs are detected and skipped; the accepted-count is read off the Accepted/ folder.

Output layout

Everything lands under target_settings["design_path"]:

<design_path>/
├── trajectory_stats.csv        # one row per trajectory
├── mpnn_design_stats.csv       # one row per MPNN sequence (per trajectory × num_seqs)
├── final_design_stats.csv      # one row per Accepted design, with Rank column
├── failure_csv.csv             # filter-failure counts (which threshold killed how many)
├── Trajectory/
│   ├── Relaxed/                # AF2-hallucinated + PyRosetta-relaxed binder PDBs (kept)
│   ├── LowConfidence/          # AF2 termination signal "LowConfidence"
│   ├── Clashing/               # AF2 termination signal "Clashing"
│   ├── Animation/              # GIF/HTML of the design trajectory (zipped at end)
│   ├── Plots/                  # PNG of trajectory metrics (zipped at end)
│   └── Pickle/                 # full ColabDesign pickle (only if save_trajectory_pickle=true)
├── MPNN/
│   ├── Sequences/              # FASTA per MPNN design (only if save_mpnn_fasta=true)
│   ├── Relaxed/                # AF2-repredicted, PyRosetta-relaxed complex PDBs
│   └── Binder/                 # AF2-repredicted binder monomer PDBs (used for Binder_RMSD)
├── Accepted/                   # PDBs of designs passing every filter
│   ├── Ranked/                 # same PDBs renamed with <rank>_<name>_model<n>.pdb
│   ├── Animation/              # trajectory animations copied for accepted designs
│   ├── Plots/                  # trajectory plots copied for accepted designs
│   └── Pickle/                 # trajectory pickles copied for accepted designs (if enabled)
└── Rejected/                   # AF2 prediction passed but a non-AF2 filter killed it

The Ranked/ folder is what you hand off — sort by final_design_stats.csv, pick the top 5–20 by hand for ordering. The README explicitly recommends ~100 final designs and ordering 5–20 of them for experimental characterisation; ipTM is a good binary binding predictor but not an affinity ranker.

Full CSV column reference and dir semantics → references/outputs.md.

Key gotchas

1. Trim the target PDB. Bigger target = quadratic GPU memory growth + more deformed-binder trajectories. Strip everything outside the targeted chains / patch / domain.
2. AF2 sees only the target, not the rest of the asymmetric unit. If the binding site is at a crystal contact, AF2 has no idea — strip neighbouring chains or pick a different epitope.
3. Expect 100s–1000s of trajectories per target. Difficult targets (large, flat, polar, β-sheet) may take 1000–3000+. The auto rejection monitor (acceptance_rate × start_monitoring) will abort the run if no designs pass; raise start_monitoring or lower acceptance_rate before assuming it's "stuck".
4. omit_AAs: "C" is on by default. Designs cannot contain cysteine (no disulfides) unless you change it. force_reject_AA: true makes this hard; default false lets MPNN keep Cys if it's the only viable choice. The peptide preset turns it on hard.
5. The "Loop" filter is inverted vs. helices. Binder_Loop% > 90 is a rejection threshold (higher: false) — too much loop = bad. If you accidentally set higher: true, you require ≥ 90% loop content (i.e., nothing will ever pass).
6. Hotspot is a hint, not a hard constraint. AF2 may pick a nearby site instead. Use Hotspot_RMSD and WrongHotspot in the failure CSV to see how often this is happening. Selecting null lets AF2 choose the site entirely — useful for cryptic pockets.
7. Multimer vs. ptm AF2. use_multimer_design: true uses AF2-multimer for hallucination and AF2-ptm for reprediction validation (and vice-versa). Mixing both as a cross-check is the whole point — don't disable it lightly.
8. predict_initial_guess rescues many failed designs. If MPNN sequences are repeatedly failing the AF2 reprediction step but the hallucinated trajectory looked good, switch to the _hardtarget variant. For large complexes (>600 aa total), additionally enable predict_bigbang.
9. β-sheet trajectories are slower. optimise_beta: true (on in most presets) triggers extra recycles when ≥ 15% β content is detected — costs time but rescues fragile β designs.
10. Resume is free. Same design_path = continue. There is no --resume flag; the loop simply skips already-present trajectory PDBs and reads accepted-count from disk.

Reference index

• references/installation.md — install_bindcraft.sh details, GPU sizing, AF2 weights layout, license notes
• references/inputs.md — full schema and syntax for the three JSON files
• references/advanced-settings.md — every advanced setting explained + which preset to pick
• references/filters.md — full filter dictionary + the five preset filter JSONs compared
• references/outputs.md — directory layout + CSV columns + how Accepted/Ranked is built
• references/troubleshooting.md — common failure modes + fixes
• examples/PDL1_target.json — drop-in target config (adapt paths)
• examples/recipes.md — copy-paste snippets for the common campaigns

Citation

Pacesa, M., Nickel, L., Schellhaas, C., Schmidt, J., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Ghosh, A., Romero, E.O., Mu, S., Dauparas, J., Mahdavi-Amiri, Y., Goverde, C.A., Khare, S.D., Kothiwale, S., Roush, W., Tate, A.J., Cho, Y., Hill, A., Goverde, C., Eberhardt, J., Aebersold, R., Levin, A., Ovchinnikov, S., Correia, B.E. (2024). BindCraft: one-shot design of functional protein binders. bioRxiv 2024.09.30.615802. https://doi.org/10.1101/2024.09.30.615802