binder-design

# 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