Run ColabFold online

Pick ColabFold when you need a fast no-MSA fold — 1-2 min per run, no MMseqs2 round-trip. Pair with AF2 standalone (D2) when you want full MSA + templates, or with ESMFold (D4) for single-sequence monomers on an even smaller GPU.

ColabFold (Mirdita et al., Nature Methods 2022) running AlphaFold2 weights without MMseqs2 MSA fetch. Faster than full AF2 at the cost of MSA-derived accuracy. Useful when you need a structure quickly and the target has a tractable fold.

When it fits:

• You need a structure in 1–2 minutes and can tolerate slightly lower accuracy than full-MSA AF2.
• You're folding many sequences sequentially and need throughput.
• Your target is a well-folded monomer or small multimer with no exotic chemistry.

Use the score legend below to read results. Each tool reports a subset of these depending on whether it does design, sequence recovery, or structure prediction.

ipTM

Predicted confidence in the binder to target interface. Higher is better. Aim above roughly 0.7 on a tractable target.

pLDDT

Per-residue confidence in the predicted fold. Higher means the model is more sure of that part of the structure.

i_pAE and pAE

Predicted alignment error, at the interface (i_pAE) or across the whole structure (pAE). Lower is better.

ProteinMPNN recovery

Fraction of native residues recovered when ProteinMPNN redesigns a known sequence on its native backbone. Higher is better; well calibrated above roughly 0.4 on diverse folds.

1-2 min per run on a dedicated GPU. Billing is by the second of compute, so a faster preset costs less.

We keep a plain-English overview of ColabFold on the main Ranomics site — what the model does under the hood, the kinds of targets it works on, and where it fits inside a full design-to-wet-lab campaign.

If you are picking between ColabFold and a sibling algorithm, these run on the same hub against the same target.

Run AlphaFold2 online

Pick AF2 when you need the gold-standard structure prediction with calibrated pLDDT + PAE. For faster single-sequence folds use ESMFold (D4); for affinity-aware folds use Boltz-2 (D6).

Run ESMFold online

Pick ESMFold when you need the fastest possible monomer fold - no MSA, no multimer, single-sequence ESM-2 language-model prediction. Pair with ColabFold (D3) for multimers or AF2 standalone (D2) for full MSA-backed accuracy.

Run ProteinMPNN online

Pick ProteinMPNN when you already have a backbone and need candidate sequences. For de novo backbone generation, use RFantibody, BindCraft, or BoltzGen first and feed the output PDB here.

Mirdita et al., Nature Methods 2022