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
bash# 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):
yamlentities: - 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:
bashmodal 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:
bash# 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
bashmodal 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
