paste this link into your ai. it will know what to do.
Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure pref
AlphaFold (via ColabFold)
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 |
Hand-off rule. When the request matches a row above, open your answer by naming that skill by its exact id (for example alterlab-boltz) and saying in one line why it fits better, then stop applying this skill's procedure. Name it even when you also need to ask for missing input. If only part of the request is out of scope, name the skill for that part and do only the in-scope part. A brief general pointer is fine; the named skill does the work.
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 3Outputs 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...GGGcolabfold_batch complex.fasta out/ --model-type alphafold2_multimer_v3Read 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.
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