---
name: alterlab-alphafold
title: alterlab alphafold
kind: skill
version: 1.1.0
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 pref
updated: 2026-11-05
authored_by: AlterLab-IEU
author_url: https://github.com/AlterLab-IEU
source_url: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/blob/main/skills/bioinformatics/alterlab-alphafold/SKILL.md
brought_by: kt
license: MIT
---

# 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

```bash
# 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:

```text
>my_complex
MKT...AAA:MSE...GGG
```

```bash
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/alphafold3` ships 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.