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Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
Passed 20 of 21 test cases with the skill, and 14 of 21 without it.
and 5 more in the full report
6 fixed15 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to predict the single-chain monomer structure for `target.fasta` using `colabfold_batch`. My colleague suggests including `--model-type alphafold2_multimer_v3` and inserting colons into the sequence. Is this recommended for monomers?
| Checked | Without skill | With skill |
|---|---|---|
| Advises omitting the --model-type flag for single-chain monomer predictions. | Missed | Passed |
| Advises omitting colon separators from single-chain monomer sequences. | Passed | Passed |
The instructions your model reads when the skill loads.
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.
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.
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
test cases passed, vs 14 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.