Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| NumPy | 1.20+ | Latest |
| RAM | 8GB | 16GB |
ipSAE (interprotein Score from Aligned Errors) is a scoring function for ranking protein-protein interactions predicted by AlphaFold2, AlphaFold3, and Boltz1. It outperforms ipTM and iPAE for binder design ranking with 1.4x higher precision in identifying true binders.
Paper: What's wrong with AlphaFold's ipTM score
git clone https://github.com/DunbrackLab/IPSAE.git
cd IPSAE
pip install numpy
python ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15
python ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10
python ipsae.py pae_model_0.npz model_0.cif 10 10
| Parameter | Description | Recommended |
|---|---|---|
| PAE file | JSON (AF2/AF3) or NPZ (Boltz) | Match predictor |
| Structure file | PDB or CIF structure | Match PAE |
| PAE cutoff | Threshold for contacts | 10-15 |
| Distance cutoff | Max CA-CA distance (A) | 10-15 |
Two output files are generated:
Chain-pair scores (_chains.csv):
chain_A,chain_B,ipSAE_min,pDockQ,pDockQ2,LIS,n_contacts,interface_dist
A,B,0.72,0.65,0.58,0.45,42,8.5
Residue-level scores (_residues.csv):
chain,resnum,pSAE,pLDDT
A,45,0.85,92.3
A,67,0.78,88.1
$ python ipsae.py scores_rank_001.json design_0.pdb 10 10
Processing design_0...
Found 2 chains: A, B
Computing ipSAE scores...
Results written to:
design_0_chains.csv
design_0_residues.csv
Summary:
ipSAE_min: 0.72
pDockQ: 0.65
LIS: 0.45
Interface contacts: 42
What good output looks like:
Should I use ipSAE?
│
├─ What are you ranking?
│ ├─ Designed binders → ipSAE ✓
│ ├─ Natural complexes → ipTM is fine
│ └─ Single proteins → Not applicable
│
├─ What predictor did you use?
│ ├─ AlphaFold2 → ipSAE ✓
│ ├─ AlphaFold3 → ipSAE ✓
│ ├─ Boltz1 → ipSAE ✓
│ ├─ Chai → ipSAE (use PAE output)
│ └─ ESMFold → Not applicable (no PAE)
│
└─ Why ipSAE over ipTM?
├─ Different length constructs → ipSAE ✓
├─ Designs with disordered regions → ipSAE ✓
└─ Standard complexes → Either works
| Metric | Standard | Stringent | Use Case |
|---|---|---|---|
| ipSAE_min | > 0.61 | > 0.70 | Primary filter |
| LIS | > 0.35 | > 0.45 | Interface quality |
| pDockQ | > 0.5 | > 0.6 | Supporting |
import subprocess
import os
from pathlib import Path
def score_designs(pae_dir, struct_dir, output_dir):
"""Score all designs in a directory."""
Path(output_dir).mkdir(exist_ok=True)
for pae_file in Path(pae_dir).glob("*_scores*.json"):
name = pae_file.stem.replace("_scores_rank_001", "")
struct_file = Path(struct_dir) / f"{name}.pdb"
if struct_file.exists():
subprocess.run([
"python", "ipsae.py",
str(pae_file),
str(struct_file),
"10", "10"
])
ls *_chains.csv | wc -l # Should match number of predictions
Low scores for good designs: Check PAE/distance cutoffs Missing output: Verify PAE file format matches predictor Inconsistent scores: Use same cutoffs across all designs
| Error | Cause | Fix |
|---|---|---|
KeyError: 'pae' | Wrong PAE format | Check if AF2/AF3/Boltz format |
FileNotFoundError | Structure not found | Verify file paths |
ValueError: no contacts | No interface detected | Check chain IDs, reduce cutoffs |
Next: Select top designs (ipSAE_min > 0.61) → experimental validation.