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Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
Passed 19 of 20 test cases with the skill, and 9 of 20 without it.
and 8 more in the full report
11 fixed10 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
When predicting a complex structure from raw FASTA sequences without precalculated alignment files, which command option automatically fetches alignments online?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies the --use_msa_server flag. | Missed | Passed |
The instructions your model reads when the skill loads.
Boltz-2 is the open-weights diffusion co-folder closest in surface to
AlphaFold3: a YAML describing protein, DNA, RNA, and ligand chains in, mmCIF
plus pTM/ipTM/pLDDT confidences out, with an optional small-molecule affinity
head. Among our four co-fold skills it is the default for binder-validation
campaigns — fully open MIT weights and the fastest sampler; pick chai1 when
you want a second independent model for consensus, openfold3 when AF3-faithful
settings matter, and esmfold2 when you can live without an MSA. Code and
weights are MIT (PyPI boltz, github.com/jwohlwend/boltz).
# complex.yaml
version: 1
sequences:
- protein:
id: A
sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRS... # target
- protein:
id: B
sequence: AIQRTPKIQVYSRHPAENG... # binder
- ligand:
id: L
smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O' # or ccd: SAH
boltz predict complex.yaml \
--use_msa_server --out_dir out/ --recycling_steps 3 --diffusion_samples 5
Each protein chain needs an MSA; without one the run exits before the model
loads. --use_msa_server queries api.colabfold.com (expect a 30–90 s pause
per chain) and is the right default unless you already have an .a3m to name
under msa: in the YAML. Setting msa: empty forces single-sequence mode —
that is an accuracy sacrifice, not a speed or memory optimization, because the
MSA search runs on CPU before the GPU stage starts.
test cases passed, vs 9 / 20 without
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