Binder Design Tool Selection Skill

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

Agent Skills: Binder Design Tool Selection

Install this agent skill to your local

Skill Files