ColabFold

You have a sequence and want its 3D shape in a minute or two, trading a little accuracy for speed. It skips the search for related natural sequences that full AlphaFold2 runs. Use it to triage a batch; use AlphaFold2 when the answer has to be right.

Predicts a structure from a sequence in one to two minutes by skipping the search for related natural sequences that full AlphaFold2 runs. Same AlphaFold2 weights, less evidence to work from — so a little less accurate and a lot faster. Useful for triaging a batch of sequences or getting a quick look at a well-behaved fold. ColabFold, Mirdita et al., Nature Methods 2022.

When it fits:

• You want a structure in a couple of minutes and can live with slightly less accuracy than full AlphaFold2.
• You are folding a batch of sequences one after another and throughput matters more than the last few points of confidence.
• Your target is a well-behaved single chain or small complex with no unusual chemistry.

You will need:

• Single-letter FASTA sequence(s).
• Targets with deep evolutionary signal. Multi-domain or low-information sequences underperform without MSA.

Each run uses a preset that sets the scale and scope:

Standalone with your FASTA

Paste a FASTA (monomer or multimer up to 600 aa total) and get pLDDT, PAE, and pTM/ipTM. ~1 to 2 min on A100-40GB. No MSA, no templates. Pair with D2 AF2 if you need the full MSA-backed fold.

Batch for many fold targets

Fold many independent targets in one job (up to 200 records). Each record can be a monomer or a multimer (use ``:`` inside a record to break chains). Per-design results stream into the job page as folds complete. Fast no-MSA tier, ~1 to 2 min per fold.

Predicted PDB with per-residue pLDDT and PAE. Download as PDB or PAE matrix for filtering.

Where a tool reports them, the scores mean:

ipTM

Predicted confidence in the contact between two chains, on a 0 to 1 scale. Higher is better: > 0.75 strong; > 0.65 acceptable. Individual tools set their own pass bar a little either side of that — this guide's own results summary above states this tool's. On a multi-chain target the number may cover the target's own chain–chain interface as well as the binder's, so read the per-tool note on the results table before comparing designs on it.

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.

Mirdita et al., Nature Methods 2022