Ipsae Agent Skill

> Binder design ranking using ipSAE (interprotein Score from Aligned Errors). (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.

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

132

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill ipsae

The instruction itself

18 sections, as written by the author

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

|--------|----------|-----------|----------|

| 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.

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How to use it

Copy the folder

Take biotender-max/ipsae from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.