alphafold2

# AlphaFold2 (ColabFold runner) This skill wraps AlphaFold2 and AlphaFold2-Multimer through `colabfold_batch`, which replaces DeepMind's local-database MSA pipeline with a call to the public MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB database mount. AF2 remains the reference monomer predictor and the multimer model is still a strong protein–protein validator, but it does not handle ligands or nucleic acids; for those, route to `boltz`, `chai1`, or `openfold3`. The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2 code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model parameters are CC-BY-4.0 with DeepMind's terms of use. ## Running it ```bash colabfold_batch input.fasta out \ --num-recycle 3 \ --model-type alphafold2_multimer_v3 ``` The input is a plain FASTA. For a complex, put every chain on one sequence line separated by `:` — `colabfold_batch` builds a paired MSA per segment and runs the multimer model when it sees the colon (so the explicit `--model-type alphafold2_multimer_v3` above is belt-and-braces). For monomers omit `--model-type` and the colon. `--templates` and `--amber` add PDB templates and OpenMM relaxation respectively; both are off by default and both add minutes per model. ColabFold runs all five AF2 model weights by default and ranks them by pLDDT (pTM/ipTM for multimer), so output per query lands in `out/` as five ranked PDBs `<name>_unrelaxed_rank_00{1..5}_*.pdb` (b-factor column carries pLDDT) and a matching `<name>_scores_rank_00{N}_*.json` with `plddt`, `ptm`, and — for multimer — `iptm` and the `pae` matrix. Rank-1 is the model to read first; ipTM > 0.5 is the usual soft pass for an interface. ## Unified-memory defaults loop forever under gVisor — the env patches them out `colabfold/batch.py` hard-sets `TF_FORCE_UNIFIED_MEMORY=1` and `XLA_PYTHON_CLIENT_MEM_FRACTION=4.0` on import. Under a gVisor sandbox unified memory is unsupported, so JAX's `device_put` loops indefinitely allocating host RAM during AF2 parameter load — the job appears hung, never errors. Override both before the import (`TF_FORCE_UNIFIED_MEMORY=0`, fraction `0.95`), or `sed`-patch the two assignments out of `batch.py` in the image build, or the first fold never starts. ## The MSA server is the wall-clock bottleneck, and it is shared `colabfold_batch` defaults to `--msa-mode mmseqs2_uniref_env`, which posts your sequence to `api.colabfold.com`. That server is a public, rate-limited resource: the wait dominates short folds and occasionally times out under load. For campaigns, run the MSA stage once with `--msa-only`, keep the resulting `.a3m` files, and feed the directory back as the input on subsequent runs — the GPU stage then starts immediately and the server is not hit again. ## Errors worth recognizing | You see | It means / do this | |---|---| | Job hangs silently during "Running model_1" with host RAM climbing | Unified-memory loop under gVisor — see the gotcha above; override or patch `batch.py`. | | `RESOURCE_EXHAUSTED` / OOM during XLA compile | `XLA_PYTHON_CLIENT_MEM_FRACTION` too high for the GPU — drop below the `0.95` default to `0.9` or so. | | MSA stage hangs at `Submitting job` | Public MMseqs2 server is rate-limiting — wait, or pre-compute with `--msa-only` and re-run from the cached `.a3m`. | --- **Next:** for designed-sequence validation, superpose the rank-1 model onto the design backbone with US-align and gate on pLDDT/ipTM thresholds; for ligand-bearing complexes, hand the same chains to `boltz` or `chai1`.