Individual skill
alphafold2
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.
resources/skills/alphafold2/SKILL.md
Source commit 357d1a980056e191f380d0e01ec4fbc7cf529d1b
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- Last checked
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- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
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# 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
