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name: scaffold-guided-design description: "Build and validate RFdiffusion scaffold-guided fold-conditioning inputs for monomers and binders, including scaffold directories, target tensors, secondary-structure masks, adjacency tensors, and sampled insertion settings." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

RFdiffusion Scaffold-Guided Design

Use this sub-skill when the user wants RFdiffusion fold conditioning from secondary-structure and block-adjacency tensors, either for monomer topology generation or binder design with a target plus scaffold library.

Route Here

Route to scaffold-guided-design for:

• Scaffold-guided monomer design with scaffoldguided.scaffoldguided=True and scaffoldguided.target_pdb=False.
• Scaffold-guided binder input preparation with scaffoldguided.target_pdb=True, target_path, target_ss, target_adj, and a binder scaffold directory.
• Creating, checking, or explaining *_ss.pt and *_adj.pt files for scaffold directories or target folds.
• Choosing scaffold_dir, scaffold_list, mask_loops, sampled_insertion, sampled_N, sampled_C, ss_mask, or systematic settings.
• Debugging mutual exclusion between scaffold_dir and per-residue secondary-structure masks.

Route elsewhere when:

• The user wants hotspot strategy, target cropping, or interface design logic; use ../binder-design/SKILL.md.
• The user wants pure monomer generation from length only; use ../unconditional-generation/SKILL.md.
• The user wants partial diffusion around an existing backbone; use ../partial-diffusion/SKILL.md.
• The user wants motif or active-site scaffolding as the primary task; use ../motif-scaffolding/SKILL.md.

Required Context

Collect these before drafting a command or validation plan:

• Runtime run_inference.py command path available to the user, not a source-checkout path from this skill.
• Output prefix and number of designs.
• Whether the workflow is monomer fold conditioning or target-bound binder fold conditioning.
• Scaffold directory containing paired NAME_ss.pt and NAME_adj.pt tensors, or explicit per-residue secondary-structure masks.
• Optional scaffold selection list, either a text file of scaffold IDs or an inline Hydra list.
• For target-bound runs: target PDB path plus optional target_ss and target_adj tensors prepared for that same target/crop.
• Sampling settings: mask_loops, sampled_insertion, sampled_N, sampled_C, and ss_mask.

Safe Input Checker

Use the bundled checker before expensive inference:

python sub-skills/scaffold-guided-design/scripts/check_scaffold_inputs.py \
  --scaffold-dir scaffolds/tim_barrel \
  --sampled-insertion 0-5 \
  --sampled-n 0-5 \
  --sampled-c 0-5

For a scaffolded binder target:

python sub-skills/scaffold-guided-design/scripts/check_scaffold_inputs.py \
  --scaffold-dir scaffolds/ppi \
  --target-pdb target.pdb \
  --target-ss target_folds/target_ss.pt \
  --target-adj target_folds/target_adj.pt \
  --hotspots A59,A83,A91 \
  --mask-loops false

The checker validates file pairing, tensor ranks, shape compatibility, scaffold list membership, target tensor presence, hotspot formatting, and common RFdiffusion assertion failures. It uses Python plus optional PyTorch; when PyTorch is absent, it still performs path and naming checks.

Command Patterns

Use references/workflows.md for full templates. Core monomer fold conditioning:

python /path/to/run_inference.py \
  inference.output_prefix=outputs/tim_barrel/design \
  scaffoldguided.scaffoldguided=True \
  scaffoldguided.target_pdb=False \
  scaffoldguided.scaffold_dir=scaffolds/tim_barrel \
  scaffoldguided.sampled_insertion=0-5 \
  scaffoldguided.sampled_N=0-5 \
  scaffoldguided.sampled_C=0-5 \
  inference.num_designs=10 \
  denoiser.noise_scale_ca=0.5 \
  denoiser.noise_scale_frame=0.5

Core scaffolded binder fold conditioning:

python /path/to/run_inference.py \
  inference.output_prefix=outputs/scaffolded_binder/design \
  scaffoldguided.scaffoldguided=True \
  scaffoldguided.target_pdb=True \
  scaffoldguided.target_path=target.pdb \
  scaffoldguided.target_ss=target_folds/target_ss.pt \
  scaffoldguided.target_adj=target_folds/target_adj.pt \
  scaffoldguided.scaffold_dir=scaffolds/ppi \
  'ppi.hotspot_res=[A59,A83,A91]' \
  scaffoldguided.mask_loops=False \
  inference.num_designs=10 \
  denoiser.noise_scale_ca=0 \
  denoiser.noise_scale_frame=0

Keep Hydra list-like values in single quotes. Prefer pilot runs with one or a few designs before scaling a large scaffold library.

Key Rules

• scaffoldguided.scaffoldguided=True selects the scaffold-guided model runner.
• scaffoldguided.scaffold_dir expects paired files named ID_ss.pt and ID_adj.pt.
• scaffoldguided.scaffold_dir is mutually exclusive with contigmap.inpaint_str_helix, contigmap.inpaint_str_strand, and contigmap.inpaint_str_loop.
• If scaffoldguided.mask_loops=False, keep sampled_insertion=0, sampled_N=0, and sampled_C=0.
• If scaffoldguided.target_pdb=True, provide scaffoldguided.target_path; target tensors are strongly recommended for scaffolded PPI and must match the target after any crop.
• scaffoldguided.scaffold_list can restrict a large scaffold directory, but every listed ID must have both tensor files.

References

• references/workflows.md: monomer, binder, scaffold-list, per-residue secondary-structure, and target-mode command templates.
• references/data-formats.md: expected tensor names, shapes, encodings, directory layouts, and optional tensor generation notes.
• references/troubleshooting.md: common RFdiffusion scaffold-guided errors and fixes.