AlphaFold2 — structure prediction from sequence
Paste a FASTA (monomer or multimer), get a predicted structure with pLDDT, PAE, and pTM/ipTM. ~5-10 min per run.
What it is for
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.
Inputs
You will need:
- Single-letter FASTA sequence(s). Multimers separated by
:or pasted as multi-record FASTA. - A stable target topology — AF2 underperforms on intrinsically disordered or flexible regions.
Each run uses a preset that sets the scale and scope:
- Standalone — your FASTA
- Paste or upload FASTA (single chain or multimer). ColabFold MMseqs2 MSA + AF2. Up to 1500 AA total across chains. ~5-10 min on A100-80GB.
- Batch — many fold targets
- Fold many independent targets in one job (up to 50 records). Each record can be a monomer or a multimer (use ``:`` to separate chains inside a record). Per-design results stream into the job page as folds complete. Slowest of the structure-prediction tools — expect ~5-10 min per fold.
Parameters you set on the form:
- Sequence
- Paste FASTA. Use
:as a chain separator for multimers (e.g.SEQ_A:SEQ_B). - Recycles
- Number of model recycles. 3 is the AF2 default; lower is faster but trades a small amount of accuracy.
Typical runtime:
- standalone
- 5–10 min
How to read the results
Predicted PDB with per-residue pLDDT, pairwise PAE, and pTM / ipTM (multimers). Download PDB or PAE matrix for downstream filtering and analysis.
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. Quick MSA-backed AF2 fold; useful for confirming the pipeline end-to-end.
- Top7 de novo design (93 aa)
- The canonical de novo designed protein (Kuhlman et al. 2003). Shows AF2 on a designed fold rather than a natural sequence.
References
Jumper et al., Nature 2021 (AF2); Mirdita et al., Nature Methods 2022 (ColabFold)