Binder Design Tool Selection

Decision framework for choosing between BoltzGen, RFdiffusion, BindCraft, and other tools for protein binder design campaigns.

Decision Tree

What do you need?
│
├── All-atom output with side-chain awareness?
│   └── YES → BoltzGen (recommended default)
│
├── Backbone-only + sequence design separately?
│   └── YES → RFdiffusion → ProteinMPNN/LigandMPNN
│
├── End-to-end with built-in AF2 validation per design?
│   └── YES → BindCraft (slower but higher hit rate)
│
├── Ligand/small molecule in binding site?
│   └── YES → BoltzGen or RFdiffusion + LigandMPNN
│
├── Peptide binder (< 30 residues)?
│   └── YES → BoltzGen (peptide-anything protocol)
│
├── Symmetric oligomer scaffold?
│   └── YES → RFdiffusion (symmetry support)
│
└── Very large target (> 500 residues)?
    └── YES → RFdiffusion (better memory efficiency)

Tool Comparison

Feature	BoltzGen	RFdiffusion	BindCraft
Side chains	Joint design	Backbone only	Joint (via AF2 hallucination)
Speed (50 designs)	~2h A100	~30 min	~8h A100
Hit rate (exp. binding)	~15–25%	~10–20%	~25–40%
Built-in validation	ipTM from diffusion	None	AF2 ipTM per design
Ligand-aware	✓	Limited	✗
Symmetric designs	✗	✓	✗
Ease of use	YAML config	CLI flags	Config JSON
GPU VRAM	24 GB	16 GB	32 GB

Campaign Scale Guide

Goal	Recommended Tool	Designs to Generate	Expected Binders to Order
Proof of concept	BoltzGen	50–100	5–10
Standard campaign	RFdiffusion + MPNN	200–500	10–20
High-quality hits	BindCraft	100–200	5–15
Ligand-binding enzyme	RFdiffusion + LigandMPNN	200–1000	10–20
Rapid screen	BoltzGen	100	10

Pipeline Templates

Standard Binder Campaign

# 1. Generate backbones
python3 RFdiffusion/scripts/run_inference.py \
    inference.output_prefix=designs/binder \
    inference.input_pdb=target.pdb \
    'ppi.hotspot_res=[A45,A67,A102]' \
    'contigmap.contigs=[A1-200/0 60-100]' \
    inference.num_designs=200

# 2. Design sequences
python3 LigandMPNN/run.py \
    --model_type proteinmpnn \
    --pdb_path designs/ \
    --out_folder seqs/ \
    --number_of_batches 8

# 3. Predict structures (ColabFold batch)
colabfold_batch seqs/all_sequences.fasta predictions/ \
    --model-type alphafold2_multimer_v3 \
    --num-recycles 3 \
    --num-models 1

# 4. Rank by ipSAE
ipsae rank predictions/ --chain-lengths 200 80 --output rankings.csv

BoltzGen All-in-One

# binder.yaml
version: 1
sequences:
  - protein:
      id: A
      sequence: MTEYKLVVVGAGGVGKS...  # target
  - protein:
      id: B
      length: 80
      design: true
constraints:
  hotspots:
    - chain: A
      residue: [45, 67, 102]

boltzgen design binder.yaml --out_dir designs/ --num_designs 100

BindCraft High-Quality

{
  "target_pdb": "target.pdb",
  "hotspot_residues": "A45,A67,A102",
  "binder_length": [60, 100],
  "num_designs": 100,
  "protocol": "default",
  "af2_validation": true,
  "output_dir": "bindcraft_out/"
}

python3 bindcraft.py --config binder_config.json

Hotspot Identification

from Bio.PDB import PDBParser
import numpy as np

def identify_hotspots(pdb_path: str, chain: str = "A",
                      binding_site_center: tuple = None,
                      radius: float = 10.0) -> list:
    """Find surface residues near a binding site for hotspot specification."""
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure("target", pdb_path)
    model = structure[0]

    hotspots = []
    for residue in model[chain]:
        if residue.id[0] != " ":  # Skip HET
            continue
        # Use CA atom as representative
        if "CA" not in residue:
            continue
        ca = residue["CA"].get_vector()

        if binding_site_center:
            center = np.array(binding_site_center)
            dist = np.linalg.norm(ca.get_array() - center)
            if dist < radius:
                hotspots.append(f"{chain}{residue.id[1]}")

    return hotspots

Avoiding Common Failures

Problem	Prevention
Designs don't contact hotspots	Verify hotspot accessibility; reduce binder length range
All designs converge to same solution	Use partial diffusion for diversity; vary hotspot sets
Poor AF2 validation scores	Use BindCraft (built-in AF2 during design)
Low expression yield	Switch to SolubleMPNN; check GRAVY/instability
Target flexibility issues	Use ensemble of target conformations