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

1. RFdiffusion for initial backbones
2. ColabDesign for refinement
3. ProteinMPNN diversification
4. AF2 final validation

Iterative refinement

1. Run initial campaign
2. Analyze failures
3. Adjust hotspots/parameters
4. Repeat with insights