ipsaelisted

# ipSAE Binder Ranking ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.8+ | 3.10 | | NumPy | 1.20+ | Latest | | RAM | 8GB | 16GB | ## Overview 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](https://www.biorxiv.org/content/10.1101/2025.02.10.637595v2) ## How to run ### Installation ```bash git clone https://github.com/DunbrackLab/IPSAE.git cd IPSAE pip install numpy ``` ### AlphaFold2 ```bash python ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15 ``` ### AlphaFold3 ```bash python ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10 ``` ### Boltz1 ```bash python ipsae.py pae_model_0.npz model_0.cif 10 10 ``` ## Key parameters | 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 | ## Output format 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,