protein-design-workflow

name protein-design-workflow description End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc. license MIT category orchestration tags ["guidance","pipeline","workflow"] Protein Design Workflow Guide Standard binder design pipeline Overview Target Preparation --> Backbone Generation --> Sequence Design | | | v v v (pdb skill) (rfdiffusion) (proteinmpnn) | | v v Structure Validation --> Filtering | | v v (alphafold/chai) (protein-qc) Phase 1: Target preparation 1.1 Obtain target structure # Download from PDB curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb" 1.2 Clean and prepare # Extract target chain # Remove waters, ligands if needed # Trim to binding region + 10A buffer 1.3 Select hotspots Choose 3-6 exposed residues Prefer charged/aromatic (K, R, E, D, W, Y, F) Check surface accessibility Verify residue numbering Output : target_prepared.pdb , hotspot list Phase 2: Backbone generation Option A: RFdiffusion (diverse exploration) modal run modal_rfdiffusion.py \ --pdb target_prepared.pdb \ --contigs "A1-150/0 70-100" \ --hotspot "A45,A67,A89" \ --num-designs 500 Option B: BindCraft (end-to-end) modal run modal_bindcraft.py \ --target-pdb target_prepared.pdb \ --hotspots "A45,A67,A89" \ --num-designs 100 Output : 100-500 backbone PDBs Phase 3: Sequence design For RFdiffusion backbones for backbone in backbones/*.pdb; do modal run modal_proteinmpnn.py \ --pdb-path " $backbone " \ --num-seq-per-target 8 \ --sampling-temp 0.1 done Output : 8 sequences per backbone (800-4000 total) Phase 4: Structure validation Predict complexes # Prepare FASTA with binder + target # binder:target format for multimer modal run modal_colabfold.py \ --input-faa all_sequences.fasta \ --out-dir predictions/ Output : AF2 predictions with pLDDT, ipTM, PAE Phase 5: Filtering and selection Apply standard thresholds import pandas as pd # Load metrics designs = pd.read_csv( 'all_metrics.csv' ) # Filter filtered = designs[ (designs[ 'pLDDT' ] > 0.85 ) & (designs[ 'ipTM' ] > 0.50 ) & (designs[ 'PAE_interface' ] < 10 ) & (designs[ 'scRMSD' ] < 2.0 ) & (designs[ 'esm2_pll' ] > 0.0 ) ] # Rank by composite score filtered[ 'score' ] = ( 0.3 * filtered[ 'pLDDT' ] + 0.3 * filtered[ 'ipTM' ] + 0.2 * ( 1 - filtered[ 'PAE_interface' ] / 20 ) + 0.2 * filtered[ 'esm2_pll' ] ) top_designs = filtered.nlargest( 50 , 'score' ) Output : 50-200 filtered candidates Resource planning Compute requirements Stage GPU Time (100 designs) RFdiffusion A10G 30 min ProteinMPNN T4 15 min ColabFold A100 4-8 hours Filtering CPU 15 min Total timeline Small campaign (100 designs): 8-12 hours Medium campaign (500 designs): 24-48 hours Large campaign (1000+ designs): 2-5 days Quality checkpoints After backbone generation Visual inspection of diverse backbones Secondary structure present No clashes with target After sequence design ESM2 PLL > 0.0 for most sequences No unwanted cysteines (unless intentional) Reasonable sequence diversity After validation pLDDT > 0.85 ipTM > 0.50 PAE_interface < 10 Self-consistency RMSD < 2.0 A Final selection Diverse sequences (cluster if needed) Manufacturable (no problematic motifs) Reasonable molecular weight Common issues Problem Solution Low ipTM Check hotspots, increase designs Poor diversity Higher temperature, more backbones High scRMSD Backbone may be unusual Low pLDDT Check design quality Advanced workflows Multi-tool combination RFdiffusion for initial backbones ColabDesign for refinement ProteinMPNN diversification AF2 final validation Iterative refinement Run initial campaign Analyze failures Adjust hotspots/parameters Repeat with insights