Overview
Predict a protein's 3D structure from its amino-acid sequence with AlphaFold2, run through ColabFold (Mirdita et al., Nature Methods 2022) — which replaces AlphaFold's slow genetic-database MSA search with the fast MMseqs2 API, making folding practical on a single GPU. Handles single chains (monomer) and complexes via AlphaFold2-Multimer (Evans et al. 2021), and reports per-residue and per-interface confidence metrics so you know which parts of a prediction to trust.
This skill runs folding and returns structures + confidence. To retrieve an
already-computed AlphaFold prediction for a known UniProt entry without running anything,
use alterlab-alphafold-db instead.
When to Use This Skill
Use this skill when the user wants to:
- Fold a protein sequence (FASTA) into a predicted 3D structure (PDB/mmCIF).
- Predict a protein complex (AF2-Multimer) and score the interface (ipTM).
- Rank multiple models and read confidence (pLDDT, pTM, PAE) to judge reliability.
- Validate a designed sequence by refolding it and checking self-consistency vs. a target.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Co-fold a protein with a ligand (SMILES/CCD) or predict binding affinity | alterlab-boltz |
| Antibody–antigen / arbitrary multi-entity complex from one FASTA | alterlab-chai |
| Look up a precomputed AlphaFold model by UniProt id | alterlab-alphafold-db |
| ESM embeddings, inverse folding, generative design | alterlab-esm |
| Dock a ligand into an existing structure | alterlab-diffdock |
| De-novo backbone generation | alterlab-rfdiffusion |
Core Capabilities
1. Monomer folding
# One sequence per FASTA record; MSAs via the hosted MMseqs2 API (--msa-mode)
colabfold_batch input.fasta out/ --num-models 5 --num-recycle 3
Outputs per record: ranked *_rank_00N_*.pdb/.cif, a JSON with plddt/pae, and
coverage/pLDDT plots. Relaxation is off by default — add --amber --num-relax 1 --use-gpu-relax to Amber-relax the top model (needs the openmm extra).
2. Complex folding (AF2-Multimer)
Join chains with a colon in one FASTA record to fold a complex:
>my_complex
MKT...AAA:MSE...GGG
colabfold_batch complex.fasta out/ --model-type alphafold2_multimer_v3
Read ipTM (interface confidence) and the inter-chain PAE block to judge whether the predicted interface is meaningful, not just the intra-chain pLDDT.
3. Confidence and validation
| Metric | Reads |
|---|---|
| pLDDT (0–100, per residue) | local confidence; <50 = likely disordered/unreliable |
| pTM | global fold confidence; >0.5 means the overall fold is plausibly right |
| ipTM | interface confidence (complexes) — the number that matters for binding. DeepMind's published bands: >0.8 confident, 0.6–0.8 gray zone, <0.6 likely failed |
| PAE | expected positional error between residue pairs; low off-diagonal = confident relative orientation |
Self-consistency check (validating a design): fold the candidate, then compare to the
intended backbone (e.g. TM-score / RMSD). A design that folds back to its target with high
pLDDT and low PAE is self-consistent — the standard acceptance gate in a
design→fold→score loop (see alterlab-proteinmpnn, alterlab-rfdiffusion).
4. Running on a GPU
Folding needs a CUDA GPU. For anything beyond a quick monomer, dispatch through
alterlab-remote-compute (SLURM or a managed GPU provider): submit colabfold_batch, poll
to completion, and harvest out/.
5. When AlphaFold 3 is the better tool
This skill runs AlphaFold 2 through ColabFold, which folds proteins and protein complexes only. AlphaFold 3 (Abramson et al., Nature 2024, doi:10.1038/s41586-024-07487-w) additionally handles ligands, nucleic acids, ions, and covalent modifications in one prediction. Access routes, as of 2026-09:
- AlphaFold Server (
alphafoldserver.com) — free for non-commercial use, with a restricted ligand/modification set and no install. - Local inference —
google-deepmind/alphafold3ships the inference pipeline under Apache-2.0; the model parameters are distributed separately by Google under their own terms of use (not Apache-2.0), so check those terms before using them in a project. - Commercial use — available through Google Cloud rather than the open weights.
If the biology is a protein–ligand or protein–nucleic-acid complex and you want an openly
licensed local model instead, alterlab-boltz (MIT) and alterlab-chai (Apache-2.0) are
the AF3-class options this suite wraps.
Resources
references/colabfold_usage.md— install/pinning, MSA modes (API vs. local DB), templates, relaxation, batch/array runs, and full metric interpretation. Loaded on demand.
Part of the AlterLab Academic Skills suite.