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Run npx skillmds@latest add majiayu000/ipsae in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ipsae safe to use?

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Which AI agents work with Ipsae?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ipsae free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ipsae by majiayu000 — Agent Skill

Name: Ipsae
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

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.

majiayu000 Updated Sep 9, 2026 567 repo stars

File contents

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

How to run

Installation

git clone https://github.com/DunbrackLab/IPSAE.git
cd IPSAE
pip install numpy

AlphaFold2

python ipsae.py scores_rank_001.json unrelaxed_rank_001.pdb 15 15

AlphaFold3

python ipsae.py fold_model_full_data_0.json fold_model_0.cif 10 10

Boltz1

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,0.72,0.65,0.58,0.45,42,8.5

Residue-level scores (_residues.csv):

chain,resnum,pSAE,pLDDT
A,45,0.85,92.3
A,67,0.78,88.1

Sample output

Successful run

$ 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:

ipSAE_min > 0.61 (primary filter)
pDockQ > 0.5 (supporting metric)
Reasonable number of interface contacts (20-100)

Decision tree

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

Recommended thresholds

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

Batch processing

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"
            ])

Verify

ls *_chains.csv | wc -l  # Should match number of predictions

Troubleshooting

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 interpretation

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.

---name: ipsaedescription: 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.license: MIT--- # 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,0.72,0.65,0.58,0.45,42,8.5 ``` **Residue-level scores** (`_residues.csv`): ``` chain,resnum,pSAE,pLDDT A,45,0.85,92.3 A,67,0.78,88.1 ``` ## Sample output ### Successful run ``` $ 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:** - ipSAE_min > 0.61 (primary filter) - pDockQ > 0.5 (supporting metric) - Reasonable number of interface contacts (20-100) ## Decision tree ``` 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 ``` ## Recommended thresholds | 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 | ## Batch processing ```python 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" ]) ``` --- ## Verify ```bash ls *_chains.csv | wc -l # Should match number of predictions ``` --- ## Troubleshooting **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 interpretation | 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.

Frequently asked questions