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

# 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 *_relaxed_rank_001_*.pdb, a JSON with plddt/pae, and coverage/pLDDT plots. TODO(verify) exact flag names against your installed ColabFold.

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
ipTM	interface confidence (complexes) — the number that matters for binding
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/.

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.