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### BindCraft Binder Design (`bindcraft-design`) Designs one or more de novo protein binders against a user-supplied target. The tool takes a target structure together with the target chain identifiers, an optional hotspot residue list, and a binder length range, and runs the BindCraft pipeline until either the requested number of accepted designs has been produced or the configured trajectory limit has been reached. The output carries each accepted binder as an amino-acid sequence, a relaxed target-binder complex `Structure` with per-residue pLDDT in the B-factor column, and the per-design BindCraft metrics used by the filter check. #### API Reference

Source Target structure. Accepts a file path, raw PDB/CIF content string, `Structure` object, or a dict in the shape produced by `Structure.model_dump(mode='json')`. Raw structure content in PDB or CIF format. Format of the content string (auto-detected if omitted). What the B-factor column represents. Optional source identifier (filepath or tool name). Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction. Chain ID(s) of the frozen target (comma-separated for multi-chain). Maps to BindCraft's `chains`. Comma-separated 1-indexed residue positions on the target that the binder must contact. Supports ranges (e.g. `"1-10,56,78"`). `None` or empty = unrestricted. `(min, max)` binder length range. Maps to BindCraft's `lengths`. Project identifier — used as a prefix in output filenames. Target accepted-design count. The pipeline stops after reaching this count or after `max_trajectories` attempts (whichever comes first).

Source Hallucination algorithm. Drives which iteration-count fields below are actually consumed (see each field's `depends_on`). Upstream Available options: `2stage`, `3stage`, `4stage`, `greedy`, `mcmc` Use AF2 multimer parameters during hallucination. Every upstream preset uses multimer. Amino acids to ban during design (no separator). Upstream default: `"C"`. Reject any design containing `omit_AAs`. Soft-stage iterations. Used by 2stage/3stage/4stage. Temporary-stage iterations. Used by 3stage/4stage. Hard-stage iterations. Used by 3stage/4stage. Greedy/MCMC iterations. Used by 2stage/4stage/greedy/mcmc. Greedy/MCMC mutation rate as % of binder length. pLDDT loss weight. Intra-chain PAE loss weight. Inter-chain (interface) PAE loss weight. Intra-chain contact loss weight. Inter-chain (interface) contact loss weight. Helicity bias weight (negative discourages helices). Interface pTM loss weight (only used when `use_i_ptm_loss=True`). Radius-of-gyration loss weight (only used when `use_rg_loss=True`). N-/C-termini distance loss weight (only used when `use_termini_distance_loss=True`). Randomize the sign of `weights_helicity` per trajectory. Enable interface pTM loss. Enable radius-of-gyration loss. Enable termini-distance loss. Intra-chain contact distance cutoff (Å). Inter-chain contact distance cutoff (Å). Number of intra-chain contacts per residue. Number of inter-chain contacts per residue. Mask target template sequence during hallucination. Mask target template sequence during validation. Mask target template side chains during hallucination. Mask target template side chains during validation. Use the trajectory structure as AF2's initial guess. Use AF2's "Big Bang" recycle initialisation. Run ProteinMPNN sequence refinement after each accepted trajectory. When False, the `mpnn_*` / `num_seqs` / `max_mpnn_sequences` / `sampling_temp` / `backbone_noise` / `model_path` fields are inert. Fix interface residues during MPNN redesign. Number of MPNN sequences to sample per trajectory. Max MPNN sequences to validate per trajectory. MPNN sampling temperature (lower = more deterministic). MPNN backbone noise. MPNN model checkpoint name. Available options: `v_48_002`, `v_48_010`, `v_48_020`, `v_48_030` MPNN weight set. Available options: `original`, `soluble` AF2 recycles during hallucination. AF2 recycles during validation. 4stage-only — increase recycles + iterations mid-trajectory when the soft-stage output is beta-heavy. Extra soft iterations for beta-heavy designs. Extra temporary iterations for beta-heavy designs. Recycles during hallucination for beta-heavy designs. Recycles during validation for beta-heavy designs. Max hallucination trajectories before stopping. `False` (upstream default) = unlimited; positive int = cap. Enable rolling acceptance-rate monitoring (stops the run if it stalls). Minimum design acceptance rate to keep running. Trajectory count before acceptance-rate monitoring starts. Per-metric threshold overrides merged on top of the upstream default filters at dispatch time. Keys are upstream metric names (e.g. `"Average_pLDDT"`); values are upstream filter dicts (e.g. `{"threshold": 0.85, "higher": True}`). Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the tool on. Maximum execution time in seconds. `None` (default) waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded.

Source Accepted binder designs (length is at most `BindCraftInput.number_of_final_designs`). Unique design identifier emitted by upstream (e.g. `"binder_l60_s12345_mpnn3"`). Designed binder amino-acid sequence (1-letter codes). Relaxed target+binder complex; B-factors are pLDDT on the 0-100 PDB scale (`b_factor_type=PLDDT`). Per-design averaged metrics that the filter check evaluates against. Random seed of the trajectory that produced this design. Amino-acid composition at the binder-target interface. 1-indexed binder residue positions at the interface. Total trajectories attempted before stopping (success or hitting `max_trajectories`). Designs that passed all filters (equals `len(designs)`). **Metrics** | Metric | Type | Range | Availability | | ---------------------------- | ----- | ------------ | ------------ | | `avg_plddt` | float | 0.0 to 1.0 | | | `avg_ptm` | float | 0.0 to 1.0 | | | `avg_iptm` | float | 0.0 to 1.0 | | | `avg_pae` | float | ≥ 0.0 | | | `avg_ipae` | float | ≥ 0.0 | | | `avg_iplddt` | float | 0.0 to 1.0 | | | `avg_ss_plddt` | float | 0.0 to 1.0 | | | `avg_binder_plddt` | float | 0.0 to 1.0 | | | `avg_binder_ptm` | float | 0.0 to 1.0 | | | `avg_binder_pae` | float | ≥ 0.0 | | | `binder_energy_score` | float | unbounded | | | `dG` | float | unbounded | | | `dSASA` | float | ≥ 0.0 | | | `dG_per_dSASA` | float | unbounded | | | `interface_sasa_pct` | float | 0.0 to 100.0 | | | `interface_hydrophobicity` | float | 0.0 to 100.0 | | | `surface_hydrophobicity` | float | 0.0 to 1.0 | | | `shape_complementarity` | float | 0.0 to 1.0 | | | `packstat` | float | 0.0 to 1.0 | | | `n_interface_hbonds` | float | ≥ 0.0 | | | `interface_hbonds_pct` | float | 0.0 to 100.0 | | | `n_interface_unsat_hbonds` | float | ≥ 0.0 | | | `interface_unsat_hbonds_pct` | float | 0.0 to 100.0 | | | `n_interface_residues` | float | ≥ 0.0 | | | `binder_helix_pct` | float | 0.0 to 100.0 | | | `binder_betasheet_pct` | float | 0.0 to 100.0 | | | `binder_loop_pct` | float | 0.0 to 100.0 | | | `interface_helix_pct` | float | 0.0 to 100.0 | | | `interface_betasheet_pct` | float | 0.0 to 100.0 | | | `interface_loop_pct` | float | 0.0 to 100.0 | | | `hotspot_rmsd` | float | ≥ 0.0 | | | `target_rmsd` | float | ≥ 0.0 | | | `binder_rmsd` | float | ≥ 0.0 | | | `unrelaxed_clashes` | float | ≥ 0.0 | | | `relaxed_clashes` | float | ≥ 0.0 | |

#### Applications This tool is appropriate for de novo binder generation against a structurally characterised target where no curated antibody scaffold or pre-existing binder is available. Representative applications include designing miniprotein binders against cell-surface receptors, generating binders that occlude a specific epitope or active site through hotspot targeting, producing structurally diverse binder candidates for downstream therapeutic engineering, and benchmarking AlphaFold2-hallucination as a binder discovery method against alternative approaches. #### Usage Tips * **Provide a hotspot residue list when targeting a defined epitope.** Set `target_hotspot_residues` to a comma-separated list of residue positions on the target structure, with ranges supported (for example `"1-10,56,78"`). Residue numbering is 1-indexed to match standard biological residue numbering conventions. Without hotspots the binder may land anywhere on the target surface. With hotspots, BindCraft biases the hallucination loss to bring the binder into contact with the specified residues. Choose functional residues such as active sites, paratope contacts, or catalytic loops rather than arbitrary surface positions. * **`binder_lengths` defaults to `(65, 150)` residues, matching the upstream default.** Binders below approximately 50 residues are effectively peptides and the AlphaFold2 multimer signal weakens. Binders above approximately 200 residues introduce significant GPU memory and per-trajectory runtime costs. Choose a tighter range to focus a campaign on a specific binder size class. * **`weights_helicity` controls the helix bias during hallucination.** The default of `-0.3` is a mild anti-helix bias chosen by the upstream authors because AlphaFold2 tends to over-produce alpha-helical bundles. Set a positive value to encourage helices for helix-friendly targets, or set `random_helicity=True` to randomise the sign per trajectory and increase secondary-structure diversity across the campaign. * **`optimise_beta=True` (the default) adds extra hallucination iterations and AlphaFold2 recycles when a trajectory looks beta-heavy.** Keep this enabled for any target that may favour beta-strand interfaces, such as immunoglobulin folds. The behaviour is gated on detected sheet content during the trajectory. * **`filter_overrides` lets you relax or tighten individual filter thresholds.** Pass a dict keyed by upstream metric name (such as `"Average_i_pTM"`) and valued as a filter dict (`{"threshold": 0.45, "higher": True}`). Only the listed metrics are overridden; every other filter keeps its upstream default. Lower the interface pTM or shape complementarity threshold first if zero designs are accepted on a hard target. * **Production runs use `number_of_final_designs=100` and `max_trajectories=False`.** This is the upstream default and produces enough accepted designs for downstream triage and experimental ordering. For a smoke test, set both to `1` together with reduced iteration counts (for example `soft_iterations=10`, `temporary_iterations=5`, `hard_iterations=2`, `greedy_iterations=2`) to verify the install and produce a single sample. * **`enable_rejection_check=True` (the default) aborts a run early if the rolling acceptance rate falls below `acceptance_rate=0.01` after `start_monitoring=600` trajectories.** Disable this gate when working on stubborn targets where you are willing to grind through many failed trajectories before the first acceptance. * **The output is iterable.** Iterating directly over the returned `BindCraftOutput` yields each accepted `BindCraftDesign` in turn, and `len(result)` returns the number of accepted designs. * **Complementary tools cover adjacent design tasks.** Reach for `proteinmpnn-sample` when an existing target-bound binder backbone only needs sequence redesign, `alphafold2-gradient` (with the Germinal backend) or a dedicated antibody-design pipeline for CDR-only redesign on a fixed antibody framework, `rfdiffusion3-design` when only a backbone is required without an accompanying sequence, and chemistry-aware ligand generation, docking, and scoring tools when the target is a small-molecule ligand rather than a protein binder.