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name: structure-and-recycling description: "Guide coordinate prediction, IPA structure refinement, confidence and recyclable outputs, and optional template or extra-MSA inputs that feed structure refinement in alphafold2-pytorch 0.4.32." disable-model-invocation: true metadata: disco-role: operating license: MIT

Structure and recycling

Use this sub-skill for residue-level coordinate prediction, invariant point attention (IPA) refinement, confidence output, recycling between model passes, or template/extra-MSA inputs that should feed the structure path. The operating contract is the alphafold2_pytorch.Alphafold2 API in distribution version 0.4.32.

Route first

• Read the API reference for verified structure flags, input shapes, return precedence, precision, and the exact Recyclables contract.
• Read the workflows for tiny coordinate, confidence, auxiliary-output, recycling, template, and extra-MSA recipes.
• Read troubleshooting for missing structure dependencies, CUDA allocation failures, shape/device problems, stale README options, and unsupported external refinement assumptions.
• Run the bundled coordinate smoke for a deterministic, download-free CPU check. Use its optional --device only after the requested backend can allocate memory.

Operating rules

1. Construct Alphafold2(..., predict_coords=True). Supply integer seq of shape (B, N), boolean residue mask of shape (B, N), and normally an integer msa of shape (B, M, N) with a matching boolean msa_mask. Omitting msa creates a one-row MSA from seq, but still requires mask.
2. At this version, coordinate output is exactly (B, N, 3): one XYZ point per residue. Do not reinterpret it as backbone, side-chain, or all-atom coordinates.
3. structure_module_depth counts IPA refinement iterations and structure_module_heads is passed to IPABlock. Although accepted by the constructor, structure_module_dim_head is not forwarded to the current IPA block and is not a verified active width control.
4. Use return_confidence=True for (coords, confidence), where confidence is raw learned-head output of shape (B, N, 1). Use return_aux_logits=True for (coords, ReturnValues). Auxiliary output wins if both flags are true, so confidence is then not returned separately.
5. Capture recycling state with both return_aux_logits=True and return_recyclables=True; then pass ret.recyclables into a compatible second call. Validate all three recyclable fields for shape, device, finiteness, and detachment first. Recycling is explicit per call and is not a guarantee of better coordinates.
6. Treat templates and extra MSA as feature tensors that feed the trunk before structure refinement, not as file paths or direct coordinate outputs. Follow the conservative current-version recipes because the extra-MSA branch has a documented implementation defect.
7. Reject stale README-only arguments including atoms, structure_module_type, structure_module_dim, structure_module_refinement_iters, and predict_real_value_distances. The current API does not expose selectable SE3/EN/EGNN structure modules or atom-expanded output.

Route core distogram/angle configuration to core-model, coordinate metrics and post-processing to utilities, and pretrained external wrappers to embeddings. Full data acquisition, training, checkpoint download, atom building, PDB production, and external relaxation remain outside this sub-skill.