Alterlab Boltz

Boltz-2 (open AlphaFold3-style co-folding)

Overview

Boltz-2 (Passaro, Wohlwend et al. 2025; jwohlwend/boltz) is an open, commercially usable biomolecular structure model in the AlphaFold3 family: it co-folds proteins together with small-molecule ligands, nucleic acids, and multiple chains in a single prediction, and can predict binding affinity — capabilities AlphaFold2/ColabFold does not have. Use it when the biology is a complex with a ligand or other molecule types, not a bare protein.

When to Use This Skill

Use this skill when the user wants to:

• Co-fold a protein with a small-molecule ligand (SMILES or CCD code) into a holo complex.
• Predict a binding affinity alongside a co-folded pose.
• Fold protein–nucleic-acid or multi-entity assemblies in one pass.
• Get an open AlphaFold3-style prediction without proprietary access.

Does NOT Trigger

Core Capabilities

1. Protein + ligand co-folding

Describe the complex in a YAML spec (chains + ligand by SMILES or CCD), then predict:

yamlCopy code
# complex.yaml
version: 1
sequences:
  - protein: { id: A, sequence: "MKT...GGG" }        # msa: path.a3m, or omit with --use_msa_server
  - ligand:  { id: L, smiles: "CC(=O)Oc1ccccc1C(=O)O" }   # or: ccd: SAH

bashCopy code
boltz predict complex.yaml --out_dir out/ --use_msa_server --use_potentials

Outputs the co-folded structure (mmCIF by default, --output_format pdb for PDB) plus a confidence_*.json per sample. --use_msa_server fetches the protein MSA from the hosted MMseqs2 server (it sends your sequence out — disclose that for unpublished work); supply msa: my.a3m per chain to stay offline, or msa: empty to force single-sequence mode. --use_potentials applies inference-time potentials that improve the physical plausibility of poses. Useful knobs: --diffusion_samples N, --recycling_steps N, --override.

2. Binding-affinity prediction

Ask for affinity in the YAML, naming the ligand chain to score:

yamlCopy code
properties:
  - affinity:
      binder: L

affinity_[input].json then carries two distinct numbers, trained on different data and meant for different jobs:

• affinity_probability_binary (0–1) — probability the ligand is a binder at all. Use it for hit discovery / binder-vs-decoy triage.
• affinity_pred_value — predicted log10(IC50) with IC50 in µM, so -3 ≈ 1 nM (strong), 0 ≈ 1 µM, 2 ≈ 100 µM. It is calibrated for comparing active molecules during hit-to-lead optimisation, not for separating actives from inactives. Convert to pIC50-style kcal/mol with (6 - y) * 1.364.

Limits worth knowing before you queue a screen: one small-molecule binder per prediction, at most 128 heavy+kept hydrogens (the training limit was ~56 atoms), and protein targets only — an RNA/DNA "target" runs without error but the number is unreliable. Treat every predicted affinity as a ranking signal and confirm against measured data (alterlab-bindingdb) or experiment.

3. Confidence and validation

Each sample gets a confidence_*.json with confidence_score (the ranking score, 0.8·complex_plddt + 0.2·iptm), ptm, iptm, ligand_iptm (interface confidence at protein–ligand interfaces specifically), protein_iptm, complex_plddt, complex_iplddt, chains_ptm and pair_chains_iptm. For a ligand pose, ligand_iptm plus the pLDDT around the pocket is the pair to read — a high overall score with a weak ligand interface means the protein folded well and the ligand did not dock convincingly. Cross-check with alterlab-diffdock when the receptor structure is already known and fixed.

4. Running on a GPU

Boltz-2 wants a CUDA GPU and downloads weights once into ~/.boltz. Point boltz predict at a directory of YAML files to batch a ligand series against one target in a single run, and dispatch it via alterlab-remote-compute: submit → poll → harvest out/predictions/.

Resources

• references/boltz_usage.md — install/pinning, YAML/FASTA input schema, MSA options, affinity output, and multi-entity examples. Loaded on demand.

Part of the AlterLab Academic Skills suite.