---

name: alphafold2 description: "Guide alphafold2-pytorch protein sequence, MSA, distogram, angle-logit, coordinate-refinement, recycling, embedding, and structure-utility workflows with version-aware troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

alphafold2-pytorch

Use this skill when a task names alphafold2-pytorch, alphafold2_pytorch, Alphafold2, Evoformer, protein sequence/MSA folding, distograms, angle logits, residue coordinates, ESM/MSA/ProtTrans embeddings, recycling, MDS, or protein-structure metrics.

This graph targets the unofficial PyTorch implementation at distribution version 0.4.32. It is not the official DeepMind AlphaFold2 implementation. Its current source and README disagree in several places; prefer the verified current API described by the linked sub-skills over older README recipes.

Install and inspect

Install the public distribution, then verify the import before building a model:

python -m pip install alphafold2-pytorch==0.4.32
python -c "import alphafold2_pytorch; print(alphafold2_pytorch.Alphafold2)"

Coordinate and utility paths also need the scientific and geometric requirements declared by the distribution, including a mutually compatible PyTorch/PyTorch3D pair, invariant-point-attention, sidechainnet, mdtraj, ProDy, mp-nerf, and OpenMM where the selected helper imports it. Use the environment checker for a read-only summary; use cross-cutting troubleshooting when dependency resolution selects an incompatible backend.

Route by task

• Core sequence/MSA trunk, Evoformer, distograms, angle logits, masks, templates, and current constructor/forward contracts: read core-model.
• Residue coordinates, invariant-point refinement, confidence, auxiliary returns, and recycling: read structure-and-recycling.
• ESM, MSA Transformer, ProtTrans wrappers or safe precomputed representations: read embeddings.
• Distogram-to-distance conversion, MDS, atom masks, sidechain layouts, Kabsch, LDDT, GDT, TM-score, and distance losses: read utilities.

Start with the owning sub-skill, then follow its API reference and troubleshooting file. Cross-links are deliberate: do not duplicate a sibling's full API table in the root router.

Operating constraints

• Use tiny synthetic tensors first. The model is quadratic in sequence length for several trunk operations, and untrained outputs are not scientific structure predictions.
• At this version, normal MSA input has shape (B, M, N) with the same residue width N as seq; masks should be boolean and on the same device.
• CPU is the default verification backend. CUDA is an optional acceleration path and must be checked on the actual host; a visible CUDA installation is not proof that a shared device has enough memory.
• Pretrained embedding wrappers may download code or weights and may require caches, network access, Hugging Face/torch.hub support, or fused operations. Do not trigger those side effects without explicit approval.
• Full training, multi-terabyte MSA acquisition, DeepSpeed sparse attention, PyRosetta relaxation, and notebook-scale experiments are intentionally not part of this runtime graph. See limitations.

Provenance and refresh

Read repository provenance before treating this graph as current for another checkout. Refresh it when the commit, package version, public signatures, or evidence paths change. The generated runtime graph is self-contained and does not require the source checkout to remain available.