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).

AlphaFold2 (Jumper et al., Nature 2021) packaged via ColabFold (Mirdita et al., Nature Methods 2022). Standard MSA-backed structure prediction with calibrated pLDDT and PAE, monomer or multimer.

When it fits:

• You need the gold-standard fold with full MSA + templates and calibrated confidence.
• Your target is monomeric or a small multimer (2–4 chains).
• You can wait ~5–10 min per run for MMseqs2 MSA fetch + 3 recycles.

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.

5-10 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 AlphaFold2 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 AlphaFold2 and a sibling algorithm, these run on the same hub against the same target.

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.

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.

Can I run AlphaFold2 multimer online without a local GPU?

Yes. Ranomics Tools runs AlphaFold2 (via the ColabFold implementation) on a dedicated GPU through your browser, with full MSA search and template support. Results land on a job page with ipTM, pLDDT, and pAE plots.

How is this different from running ColabFold yourself?

Same underlying weights and pipeline — but you skip CUDA setup, MMseqs2 round-trip on your laptop, and the wait for the public Colab queue. You also get a persistent job page you can share or hand off into ProteinMPNN or BindCraft.

How much does an AlphaFold2 multimer run cost?

Billing is by the second of dedicated GPU time. A typical single-complex fold costs a few cents to a dollar from your wallet. New accounts start with $5 of credit, which covers many monomer folds or a handful of multimers.

Jumper et al., Nature 2021 (AF2); Mirdita et al., Nature Methods 2022 (ColabFold)