ipsae
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.
npx skills add majiayu000/claude-skill-registry --skill ipsae-adaptyvbio-protein-design-skill-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Prerequisites
- Overview
- How to run
- Installation
- AlphaFold2
- AlphaFold3
- Boltz1
- Key parameters
- Output format
- Sample output
- Successful run
- Decision tree
- Recommended thresholds
- Batch processing
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 ls *_chains.csv | wc -l # Should match number of predictions
What does the ipsae skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ipsae-adaptyvbio-protein-design-skill-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.