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

Scenario	Use instead
Protein-only or protein–protein folding, no ligand	alterlab-alphafold
Antibody–antigen / general one-FASTA multi-entity complex	alterlab-chai
Dock a ligand into an existing, fixed receptor structure	alterlab-diffdock
Retrieve an experimentally determined structure	alterlab-pdb
Design a binding-pocket sequence around a ligand	alterlab-ligandmpnn

Core Capabilities

1. Protein + ligand co-folding

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

# complex.yaml (schema — TODO(verify) against installed boltz)
version: 1
sequences:
  - protein: { id: A, sequence: "MKT...GGG" }
  - ligand:  { id: L, smiles: "CC(=O)Oc1ccccc1C(=O)O" }

boltz predict complex.yaml --out_dir out/ --use_msa_server

Outputs the co-folded structure (protein + placed ligand) plus per-model confidence. --use_msa_server fetches the protein MSA from the hosted service (disclose for sensitive sequences); a local MSA can be supplied instead.

2. Binding-affinity prediction

Boltz-2 can predict a binding-affinity value for a protein–ligand pair alongside the pose — useful for triage/ranking in virtual screening. Treat predicted affinities as a ranking signal, not a measured constant; confirm hits experimentally or against measured data (alterlab-bindingdb). TODO(verify) the exact affinity-output flag/field per version.

3. Confidence and validation

Read the per-model confidence (and, for the interface, the model's interface score) to decide which pose to trust. For a ligand pose specifically, sanity-check that the ligand sits in a plausible pocket and that protein confidence around the site is high. Cross-check a docked alternative with alterlab-diffdock when the receptor structure is already known and fixed.

4. Running on a GPU

Boltz-2 needs a CUDA GPU and downloads weights once. Batch predictions (e.g. a ligand series against one target) via alterlab-remote-compute: submit → poll → harvest out/.

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.