ColabFold — fast no-MSA fold
Paste a FASTA, get a predicted structure with pLDDT and PAE. No-MSA speed tier — ~1-2 min per run.
What it is for
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.
Inputs
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 — your FASTA
- Paste a FASTA (monomer or multimer up to 600 aa total) and get pLDDT + PAE + pTM/ipTM. ~1-2 min on A100-40GB. No MSA, no templates — pair with D2 AF2 if you need the full MSA-backed fold.
- Batch — 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 — ~1-2 min per fold.
Parameters you set on the form:
- Sequence
- Paste FASTA. Use
:as a chain separator for multimers. - Recycles
- Model recycles. ColabFold default is 3; reduce for speed if your target's fold is well-known.
Typical runtime:
- standalone
- 1–2 min
How to read the results
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 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.
Try these examples
One-click sample inputs that load straight into the run form. Edit any field before submitting.
- Ubiquitin (76 aa)
- Tiny monomer benchmark. ~1 min on the no-MSA ColabFold path; useful for confirming the pipeline end-to-end.
- Top7 de novo design (93 aa)
- Canonical de novo designed protein. Shows ColabFold's no-MSA path on a designed fold.
References
Mirdita et al., Nature Methods 2022