ReddRadar
Agent skill
binder-design
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.).
Install this agent skill to your Project
npx add-skill https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binder-design
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
| 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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
adaptyvbio/protein-design-skills
proteinmpnn
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
adaptyvbio/protein-design-skills
campaign-manager
Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.
adaptyvbio/protein-design-skills
esm
ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
adaptyvbio/protein-design-skills
binding-characterization
Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
adaptyvbio/protein-design-skills
cell-free-expression
Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
adaptyvbio/protein-design-skills
ligandmpnn
Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.
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