# AlphaFold2 Structure Prediction

Predicts monomer and multimer protein structures with ColabFold and scores them.

- Repository: https://github.com/aipoch/open-science (5414 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aipoch/open-science --skill alphafold2 -g -a claude-code -y`
Codex: `npx skills add aipoch/open-science --skill alphafold2 -g -a codex -y`
Cursor: `npx skills add aipoch/open-science --skill alphafold2 -g -a cursor -y`
Gemini CLI: `npx skills add aipoch/open-science --skill alphafold2 -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/aipoch/open-science/blob/HEAD/resources/skills/alphafold2/SKILL.md