AlphaFold2 Structure Prediction
Predicts monomer and multimer protein structures with ColabFold and scores them.
Install
npx skills add aipoch/open-science --skill alphafold2 -g -a claude-code -y- Repository
- aipoch/open-science5.4k
- Category
- Data and ML
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,000 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
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.
