| name | alterlab-alphafold |
| description | 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 prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite. |
| license | MIT |
| allowed-tools | Read Write Edit Bash(python:*) Bash(uv:*) |
| compatibility | Runs via ColabFold (`colabfold_batch`; install `colabfold[alphafold]` — TODO(verify) exact pin) under `uv run python`. Requires a CUDA GPU for folding (JAX/CUDA); the MSA step uses the hosted MMseqs2 API by default or a local database. AF2 network weights are downloaded once and cached (~several GB). Dispatch heavy runs via alterlab-remote-compute. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
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 |
Core Capabilities
1. Monomer folding
colabfold_batch input.fasta out/ --num-models 5 --num-recycle 3
Outputs per record: ranked *_relaxed_rank_001_*.pdb, a JSON with /, and
coverage/pLDDT plots. exact flag names against your installed ColabFold.
