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skills/FreedomIntelligence/OpenClaw-Medical-Skills/binder-design

binder-design

Official1
FreedomIntelligence/OpenClaw-Medical-Skills·Biology Medicine and Bioinformatics·Audit passed·Snapshot 22c4e6c58788

Summary

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.).

SKILL.md

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

ToolStrengthsWeaknessesBest For
BoltzGenAll-atom, single-step, ligand-awareHigher GPU requirementStandard (recommended)
BindCraftEnd-to-end, built-in AF2 validationLess diverseProduction campaigns
RFdiffusionHigh diversity, fastRequires ProteinMPNNExploration, diversity
GerminalNanobody/VHH designSpecializedAntibody 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
Installs
0
  • 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

StageCountPurpose
Backbone generation500-1000Diversity
Sequences per backbone8-16Sequence space
AF2 predictionsAllValidation
After filtering50-200Candidates
Experimental testing10-50Final 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

StepCompute Time
RFdiffusion (500 designs)2-4 hours
ProteinMPNN (8000 sequences)1-2 hours
AF2 prediction (8000 sequences)12-24 hours
Filtering and analysis1-2 hours

Total: 1-2 days of compute

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