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):

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:

bash
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:

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

bash
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