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name binder-design
description Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
license MIT
category orchestration
tags
guidance
tool-selection
workflow

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