BoltzGen All-Atom Design

Prerequisites

How to run

Local installation

git clone https://github.com/HannesStark/boltzgen.git
cd boltzgen
pip install -e .

Binder design according to the input YAML file

boltzgen run example/vanilla_protein/1g13prot.yaml \
  --output workbench/test_run \
  --protocol protein-anything \
  --num_designs 10 \
  --budget 2
# --num_designs is the number of intermediate designs. In practice you will want between 10,000 - 60,000
# --budget is how many designs should be in the final diversity optimized set

YAML configuration

BoltzGen uses an entity-based YAML format to specify what to design and what the target is.

Important notes:

• Residue indices use label_seq_id (1-indexed), not auth_seq_id
• File paths are relative to the YAML file location
• Run boltzgen check config.yaml to verify before running
• View in Molstar to confirm binding site is correctly specified

Entity Types

Designed Protein

entities:
  - protein:
      id: B                    # Chain ID for designed protein
      sequence: 80..140        # Variable length (80-140 residues)

Sequence specification:

• 80..140 - random length between 80 and 140 residues
• 80 - exactly 80 designed residues
• AAAVVV20PPP - specific residues with 20 designed in middle
• 3..5C6C3 - designed residues with specific cysteines

Target from File

entities:
  - file:
      path: target.cif        # CIF or PDB file (relative to YAML)
      include:                 # Which chains/residues to include
        - chain:
            id: A
            res_index: 2..50,55..  # Optional: specific residues
      binding_types:           # Where design should bind
        - chain:
            id: A
            binding: 45,67,89  # Binding site residues
      structure_groups: "all"  # Optional: structure specification

Non-Designed Protein

entities:
  - protein:
      id: X
      sequence: AAVTTTTPPP    # Fixed sequence (not designed)

Constraints (Bonds)

constraints:
  - bond:
      atom1: [S, 11, SG]      # [chain_id, res_index, atom_name]
      atom2: [S, 18, SG]      # Disulfide bond

Protocol-Specific Examples

Protein Binder Design (protein-anything)

entities:
  # Designed binder (80-140 residues)
  - protein:
      id: B
      sequence: 80..140

  # Target protein
  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 45,67,89

Peptide Design (peptide-anything)

entities:
  # Designed peptide (12-20 residues)
  - protein:
      id: G
      sequence: 12..20

  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 343,344,251
      structure_groups: "all"

Cyclic Peptide with Disulfide

entities:
  - protein:
      id: S
      sequence: 10..14C6C3    # Designed with cysteines

  - file:
      path: target.cif
      include:
        - chain:
            id: A

constraints:
  - bond:
      atom1: [S, 11, SG]
      atom2: [S, 18, SG]

WHL Stapled Peptide

entities:
  - protein:
      id: R
      sequence: 3..5C6C3

  - ligand:
      id: Q
      ccd: WHL

  - file:
      path: target.cif
      include:
        - chain:
            id: A

constraints:
  - bond:
      atom1: [R, 4, SG]
      atom2: [Q, 1, CK]
  - bond:
      atom1: [R, 11, SG]
      atom2: [Q, 1, CH]

Small Molecule Binding (protein-small_molecule)

entities:
  - protein:
      id: A
      sequence: 100..150

  - ligand:
      smiles: "CCO"           # Ethanol
      # or ccd: ATP           # From CCD database

Nanobody Design (nanobody-anything)

entities:
  - protein:
      id: H
      sequence: EVQLVESGG...  # Framework with designed CDRs
      # Use specific residue notation for CDR design

  - file:
      path: antigen.cif
      include:
        - chain:
            id: A

Advanced Options

Partial Target Flexibility

entities:
  - file:
      path: target.cif
      include:
        - chain:
            id: A
      structure_groups:
        - group:
            visibility: 1     # Fixed structure
            id: A
            res_index: 10..50
        - group:
            visibility: 0     # Flexible (not structurally specified)
            id: A
            res_index: 51..60

Redesign Existing Residues

entities:
  - file:
      path: complex.cif
      include:
        - chain:
            id: A
      design:                  # Residues to redesign
        - chain:
            id: A
            res_index: 14..19

Secondary Structure Constraints

entities:
  - file:
      path: target.cif
      design:
        - chain:
            id: A
            res_index: 14..19
      secondary_structure:
        - chain:
            id: A
            helix: 15..17
            sheet: 19
            loop: 14

Not-Binding Regions

entities:
  - file:
      path: target.cif
      include:
        - chain:
            id: A
        - chain:
            id: B
      binding_types:
        - chain:
            id: A
            binding: 45,67,89
        - chain:
            id: B
            not_binding: "all"  # Design should NOT bind here

Design protocols

Output format

output/
├── sample_0/
│   ├── design.cif         # All-atom structure (CIF format)
│   ├── metrics.json       # Confidence scores
│   └── sequence.fasta     # Sequence
├── sample_1/
│   └── ...
└── summary.csv

Note: BoltzGen outputs CIF format. Convert to PDB if needed:

from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("design", "design.cif")
io = PDBIO()
io.set_structure(structure)
io.save("design.pdb")

Sample output

Successful run

$ modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 10
Running: boltzgen run binder.yaml --output /tmp/out --protocol protein-anything --num_designs 10
[INFO] Loading BoltzGen model...
[INFO] Generating designs...
[INFO] Running inverse folding...
[INFO] Running structure prediction...
[INFO] Filtering and ranking...
[INFO] Pipeline complete

Results saved to: ./out/boltzgen/2501161234/

Output directory structure:

out/boltzgen/2501161234/
├── intermediate_designs/           # Raw diffusion outputs
│   ├── design_0.cif
│   └── design_0.npz
├── intermediate_designs_inverse_folded/
│   ├── refold_cif/                 # Refolded complexes
│   └── aggregate_metrics_analyze.csv
└── final_ranked_designs/
    ├── final_10_designs/           # Top designs
    └── results_overview.pdf        # Summary plots

What good output looks like:

• Refolding RMSD < 2.0A (design folds as predicted)
• ipTM > 0.5 (confident interface)
• All designs complete pipeline without errors

Decision tree

Should I use BoltzGen?
│
└─ What type of design?
   ├─ All-atom precision needed → protein-structure-design-boltzgen ✓
   ├─ Ligand binding pocket → protein-structure-design-boltzgen ✓
   └─ Antibody or nanobody design  → antibody-design-iggm

Typical performance

Per-design: ~30-60s for typical binder.

Verify

find output -name "*.cif" | wc -l  # Should match num_samples

Troubleshooting

Verify config first: Always run boltzgen check config.yaml before running the full pipeline Slow generation: Use fewer designs for initial testing, then scale up OOM errors: Use A100-80GB or reduce --num-designs Wrong binding site: Residue indices use label_seq_id (1-indexed), check in Molstar viewer

Error interpretation

Next: Validate with structure-prediction-boltz-2.