Run AlphaFold2 online

Pick AF2 when you need the gold-standard structure prediction with calibrated pLDDT and 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 and templates and calibrated confidence.
• Your target is monomeric or a small multimer (2 to 4 chains).
• You can wait roughly 5 to 10 min per run for MMseqs2 MSA fetch plus 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 at an interface. Higher is better; aim above roughly 0.7 on a tractable target. On a multi-chain target it may cover the target's own chain–chain interface as well as the binder's.

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 to 10 min per run on a dedicated GPU. You pay only for the compute a job delivers, drawn from your wallet balance.

We keep a plain-English overview of AlphaFold2 on the main Ranomics site. It covers 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 to 2 min per run, no MMseqs2 round-trip. Pair with AF2 standalone (D2) when you want full MSA and 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)