ESMFold

You have one protein sequence and want its 3D shape in about 30 seconds. No search for relatives, so it works on designed or orphan sequences that have no natural family to align against. One chain only — for complexes use ColabFold or AlphaFold2.

Predicts the structure of one protein chain from its sequence alone, in about 30 seconds. It reads the sequence through a protein language model instead of searching for relatives, so it still works on designed or orphan sequences that have no natural family to align against. One chain only — it cannot fold a complex. ESMFold, Lin et al., Science 2023.

When it fits:

• You want a single-chain fold in well under a minute.
• Your sequence is designed, or has no known relatives, so a search for them would come back empty anyway.
• You are triaging a large batch of sequences and need throughput more than precision.

You will need:

• Single FASTA sequence (monomer only).
• Sequence length under ~600 residues for best accuracy.

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

Standalone with your FASTA

Paste a single-chain FASTA (10 to 400 aa monomer) and get pLDDT plus predicted structure. ~30 s on A100-40GB once the 3B model is warm. No MSA, no multimer. Pair with ColabFold (D3) or AF2 (D2) when you need those.

Batch for many monomers

Fold many monomer sequences (FASTA records or one per line) in a single job, up to 500 records. Each fold is shipped back through the partial-results contract; the results table renders per-design pLDDT, PDB download and NGL viewer as folds complete. Cost scales linearly with batch size.

Predicted PDB with per-residue pLDDT. No PAE (single-sequence prediction has no inter-domain signal). Use as a fast self-consistency check on designed sequences.

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.

Lin et al., Science 2023