Binder Design Tool Selection
Decision tree
De novo binder design?
│
├─ Standard target → BoltzGen (recommended)
│ All-atom output (no separate ProteinMPNN step needed)
│ Better for ligand/small molecule binding
│ Single-step design (backbone + sequence + side chains)
│
├─ Need diversity/exploration → RFdiffusion + ProteinMPNN
│ Maximum backbone diversity
│ Two-step: backbone then sequence
│
├─ Integrated validation → BindCraft
│ Built-in AF2 validation
│ End-to-end pipeline
│
├─ Ligand binding → BoltzGen ✓
│ All-atom diffusion handles ligand context
│
├─ Peptide/nanobody → Germinal
│ VHH/nanobody design
│ Germline-aware optimization
│
└─ Antibody/Nanobody
+-- VHH design --> germinal skill
Tool comparison
| Tool | Strengths | Weaknesses | Best For |
|---|
| BoltzGen | All-atom, single-step, ligand-aware | Higher GPU requirement | Standard (recommended) |
| BindCraft | End-to-end, built-in AF2 validation | Less diverse | Production campaigns |
| RFdiffusion | High diversity, fast | Requires ProteinMPNN | Exploration, diversity |
| Germinal | Nanobody/VHH design | Specialized | Antibody optimization |
Recommended Pipeline: BoltzGen → Chai → QC
BoltzGen provides all-atom design with built-in side-chain packing:
Target → BoltzGen → Validate → Filter
(pdb) (all-atom) (chai) (qc)
1. Target preparation
# Fetch structure from PDB
# Use pdb skill for guidance
- Trim to binding region + 10A buffer
- Remove waters and ligands
- Renumber chains if needed
2. Hotspot selection
- Choose 3-6 exposed residues
- Prefer charged/aromatic residues
- Cluster spatially (within 10-15A)
3. Design with BoltzGen (Recommended)
First, create a YAML config file (e.g., binder.yaml):
entities:
- protein:
id: B
sequence: 70..100
- file:
path: target.cif
include:
- chain:
id: A
binding_types:
- chain:
id: A
binding: 45,67,89
Then run:
modal run modal_boltzgen.py \
--input-yaml binder.yaml \
--protocol protein-anything \
--num-designs 50
Why BoltzGen?
- All-atom output (no separate ProteinMPNN step needed)
- Better for ligand/small molecule binding
- Single-step design (backbone + sequence + side chains)
4. Alternative: RFdiffusion Pipeline
For maximum diversity or when backbone-only is preferred:
# Step 1: Backbone generation
modal run modal_rfdiffusion.py \
--pdb target.pdb \
--contigs "A1-150/0 70-100" \
--hotspot "A45,A67,A89" \
--num-designs 500
# Step 2: Sequence design
modal run modal_ligandmpnn.py \
--pdb-path backbone.pdb \
--num-seq-per-target 16 \
--sampling-temp 0.1
5. Validation
modal run modal_chai1.py \
--input-faa sequences.fasta \
--out-dir predictions/
6. Filtering
Apply standard thresholds:
- pLDDT > 0.80
- ipTM > 0.50
- PAE_interface < 10
- scRMSD < 2.0 A
See protein-qc skill for details.
Number of designs
| Stage | Count | Purpose |
|---|
| Backbone generation | 500-1000 | Diversity |
| Sequences per backbone | 8-16 | Sequence space |
| AF2 predictions | All | Validation |
| After filtering | 50-200 | Candidates |
| Experimental testing | 10-50 | Final selection |
Common mistakes
Wrong hotspots
- Using buried residues
- Too many hotspots (over-constrain)
- Wrong chain/residue numbers
Insufficient diversity
- Too few designs generated
- Low temperature in ProteinMPNN
- Not exploring multiple backbones
Poor target preparation
- Including full protein instead of binding region
- Missing important structural features
- Wrong protonation states
Timeline guide
| Step | Compute Time |
|---|
| RFdiffusion (500 designs) | 2-4 hours |
| ProteinMPNN (8000 sequences) | 1-2 hours |
| AF2 prediction (8000 sequences) | 12-24 hours |
| Filtering and analysis | 1-2 hours |
Total: 1-2 days of compute