> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tamarind.bio/llms.txt
> Use this file to discover all available pages before exploring further.

# BoltzGen

> AI-powered protein and peptide binder design

BoltzGen designs proteins, peptides, nanobodies, and other molecules that bind to target structures or small molecules. Achieves 60-70% success rates for nanobody and protein binders against novel targets with only 15 designs tested.

## Experimental Validation

Validated across 8 independent wetlab campaigns spanning diverse targets and modalities.

### Novel Targets (No Similar Bound Structures in PDB)

**Nanobody & Protein Design**: 66% success rate (6/9 targets with nM binders) testing ≤15 designs per target

| Target | Nanobody Best Kd | Protein Best Kd |
| ------ | ---------------- | --------------- |
| PHYH   | 7.8 nM           | 22 nM           |
| PMVK   | 6.1 nM           | 10 nM           |
| RFK    | 8.8 nM           | -               |
| MZB1   | 120 nM           | 9.8 nM          |
| AMBP   | -                | 53 nM           |
| IDI2   | 520 nM           | 26 nM           |
| HNMT   | 99 nM            | -               |
| GM2A   | -                | 270 nM          |

### Benchmark Targets

**80% success rate** for both nanobodies and proteins against 5 clinical targets (IL-7Rα, InsulinR, PDGFR, PD-L1, TNFα)

### Other Modalities

**Bioactive Peptides**: nM-μM binders for melittin, indolicidin, protegrin (6 designs per target)

**RagC Linear Peptides**: 7/29 designs bound (best: 3.5 μM)

**RagA:RagC Cyclic Peptides**: 14/24 designs bound (best: 80 μM)

**NPM1 Disordered Region**: 1/5 designs showed nucleolar localization in live cells

**Small Molecules**: Weak binding (30-250 μM) for rucaparib and rhodamine derivative

**GyrA Antimicrobials**: 19.5% of 1,808 designs inhibited E. coli growth >4×

## Design Tasks

### Nanobody Design

Design single-domain antibodies against protein targets.

**Required:**

* Target PDB file
* Target chains (optional, defaults to all)

**Optional:**

* Binding site residues
* Scaffold type: Default (de novo) or Custom (optimize existing framework)

**Custom Framework Options:**

* Framework input type: Structure or Sequence
* For structure input: Framework PDB file, framework chain, CDR regions to design
* For sequence input: Framework sequence with CDR placeholders using number ranges (e.g., `EVQLVESGGGLVQPGGSLRLSCAASG5..10WVRQAPGKGLEWVS8..12RFTISRDNSKNTLYLQMNSLRAEDTAVYYC10..20WGQGTLVTVSS`)
* CDR exclude counts: Residues to remove from start of each CDR (e.g., "3,3,7")
* CDR insertion lengths: Ranges for new residues per CDR (e.g., "1-5,1-5,1-14")

### Antibody Design

Design antibodies with heavy and light chains against protein targets.

**Required:**

* Target PDB file
* Target chains (optional, defaults to all)

**Optional:**

* Binding site residues
* Scaffold type: Default (de novo) or Custom (optimize existing framework)

**Custom Framework Options:**

* Framework input type: Structure or Sequence
* For structure input: Framework PDB file, heavy chain ID, light chain ID, heavy/light CDR regions
* For sequence input: Heavy chain framework sequence and light chain framework sequence with CDR placeholders
* Heavy/Light CDR exclude counts and insertion length ranges

### Protein Binder Design

Design general protein binders.

**Required:**

* Target PDB file
* Length range (e.g., 100-150)

**Optional:** Target chains, binding site, number of designs, batches, budget

### Peptide Design

Design linear or cyclic peptides.

**Required:**

* Target PDB file
* Length range (e.g., 10-20)

**Optional:** Target chains, binding site, cyclic flag, number of designs, batches, budget

### Cyclotide Design

Design cyclic peptides with disulfide bonds.

**Required:**

* Target PDB file
* Cyclotide sequence specification with cysteines for disulfide bonds (e.g., "3C8C6C5C3C1C2" means 3 residues, Cys, 8 residues, Cys, etc.)
* Disulfide bond pairs (positions of cysteine pairs to form bonds)

**Optional:** Target chains, binding site, number of designs, batches, budget

### Small Molecule Binder Design

Design proteins that bind small molecules.

**Required:**

* Target SMILES string
* Length range (e.g., 100-150)

**Optional:** Number of designs, batches, budget

**Note**: Currently achieves weak (30-250 μM) binding; consider 10,000+ designs for better results

### Custom Design

Advanced mode for complex designs with multiple entities and constraints.

**Required:**

* **Entities**: List of proteins and ligands with:
  * Type: "protein" or "ligand"
  * Chain ID
  * Sequence (use numbers for designed regions: "15..20", letters for fixed: "AAVTT15")
  * CCD Code or SMILES (for ligands)

**Optional:** Binding types, secondary structure, cyclic flag, constraints (disulfide bonds), number of designs, batches, budget

**Use Cases:** Disulfide-bonded cyclic peptides, stapled peptides, multi-chain complexes

### YAML Configuration

Provide a custom YAML configuration file with associated structure files for advanced control.

**Required:**

* YAML configuration file
* Structure file(s) (PDB or CIF format)
* Protocol selection: Nanobody, Protein, Small Molecule Binder, or Peptide

## Common Parameters

**Number of Designs:**

* Default: 10
* Range: 1-100,000
* For large runs, design batching automatically splits into multiple jobs

**Design Batching:**

* Auto-enabled when number of designs > 10
* Configurable batch size (default: 10 designs per job)

**Budget:**

* Final number of designs optimized for diversity and quality
* Default: 2

**Omit Amino Acids:**

* Amino acids to exclude from the design
* Default: C (cysteine) for peptide and nanobody design, none for others
* Use "empty" to include all amino acids

## Best Practices

**Starting Out:**

* Test run: 10-50 designs
* Production: 10,000-60,000 designs for challenging targets
* Budget: 2-100 final diverse designs

**Target Selection:**

* Binding sites should have ≥3 hydrophobic residues
* Avoid heavily glycosylated regions
* Specify binding site when possible

**Expected Success Rates:**

* Nanobodies/Proteins: 60-70% (nM affinity) for novel targets
* Peptides: Lower affinity (μM range) but good hit rates
* Small molecules: Challenging (μM affinity)

**Troubleshooting:**

* No successes: Try different binding sites or longer runs
* Low expression: Check for hydrophobic patches
* **Avoid lengths 73-76**: Known memorization issue (generates ubiquitin)

## Pipeline Output

When the pipeline completes, your output directory will contain:

### Configuration Files

* `config/` — Configuration files
* `steps.yaml` — Pipeline steps configuration

### Initial Designs

`intermediate_designs/` — Output of design step

* `*.cif` — CIF structure files for designed proteins and targets before inverse folding
* `*.npz` — Metadata files for designs

### Processed Designs

`intermediate_designs_inverse_folded/` — Output of inverse folding, folding, and analysis steps

* `*.cif` — CIF files after inverse folding (designed residues have backbone atoms only; sidechain coordinates are 0,0,0)
* `*.npz` — Metadata files
* `refold_cif/` — Refolded complex structures (target + binder). Primary input for analysis and filtering
* `refold_design_cif/` — Refolded binder structures without target
* `aggregate_metrics_analyze.csv` — Aggregated metrics across all designs
* `per_target_metrics_analyze.csv` — Metrics per target

### Final Results

`final_ranked_designs/` — Output of filtering step

* `intermediate_ranked_[N]_designs/` — Top-N quality designs (CIFs copied from refold\_cif/)
* `final_[budget]_designs/` — Final quality + diversity set (CIFs copied from refold\_cif/)
* `all_designs_metrics.csv` — Metrics for all designs considered by filtering
* `final_designs_metrics_[budget].csv` — Metrics for selected final set
* `results_overview.pdf` — Visualization plots

### Key Metrics in CSV Files

**Quality Metrics:**

* `design_ptm` — Predicted TM-score for designed structure (higher = better, >0.75 recommended)
* `design_iptm` — Predicted TM-score for design-target interactions (higher = better)
* `filter_rmsd` — RMSD to refolded structure (lower = better, \<2.5Å recommended)

**Interface Metrics:**

* `plip_hbonds_refolded` — Number of hydrogen bonds between design and target
* `plip_saltbridge_refolded` — Number of salt bridge interactions
* `delta_sasa_refolded` — Change in solvent accessible surface area (higher = better burial)

**Developability Metrics:**

* `design_hydrophobicity` — Hydrophobicity score of designed residues
* `design_largest_hydrophobic_patch_refolded` — Area of largest hydrophobic patch (lower = better)
* `liability_score` — Overall developability score (lower = better)

**Ranking:**

* `final_rank` — Final ranking position (1 = best)
* `pass_filters` — Binary flag indicating whether design passed all filters

## Runtime

Approximate time per design for \~200 residues:

* \~60 sec (generation)
* \~5 sec (inverse folding)
* \~60 sec (structure prediction)
* \~20 sec total (filtering all designs)

[Try BoltzGen](https://app.tamarind.bio/tools/boltzgen)