Alterlab boltz

Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/bioinformatics/alterlab-boltz

239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministic citation verifier, research→write→review→publish pipeline, and a skill-finder front door. Claude Code, Cursor, Codex, Gemini CLI & Copilot.

Install

npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-boltz

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

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Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.3 KB, as published. Nobody here has run it

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.