AlphaJudge: I am the score!

AlphaJudge evaluates AlphaFold-predicted protein complexes by merging AI-derived confidences (ipTM, pTM, iptm+ptm/confidence_score, pLDDT, PAE) with fast, self-contained interface biophysics (contacts, H-bonds, salt bridges, buried area, solvation proxy, shape complementarity) into a tidy CSV for downstream analysis.

⚠️ Disclaimer
Interface biophysical scores in AlphaJudge have not yet been validated against CCP4/PISA and are intended for relative ranking, not quantitative biophysical interpretation.

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What it does

AlphaJudge parses AF2 and AF3 outputs and summarizes per-model / per-interface metrics:

category	metrics (examples)	notes
AlphaFold internal	ipTM, pTM, iptm+ptm/confidence_score, avg interface PAE, avg interface pLDDT	unified for AF2/AF3
physical & geometric	buried area, contact pairs, H-bonds, salt bridges, interface composition	self-contained
derived scores	pDockQ, pDockQ2, mpDockQ, ipSAE, LIS, interface score	implemented here

Use cases: rank poses, sanity-check AF confidences, or export features for ML.

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Pipeline overview

AlphaFold models (AF2 or AF3)  →  AlphaJudge  →  interfaces.csv

• Detects AF2 vs AF3 automatically from the run directory
• Loads structure and confidences, computes interface descriptors
• Writes interfaces.csv into the same directory

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Installation

Create conda/mamba env

git clone https://github.com/KosinskiLab/AlphaJudge.git
cd AlphaJudge
mamba env create -f environment.yaml
mamba activate alphajudge

Then, pip install in the existing environment

pip install .

or pip editable install in existing environment

pip install -e .

Requirements: Python ≥3.10; runtime deps are biopython, numpy, matplotlib (installed automatically with pip install .).

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CLI usage

The package exposes an alphajudge entry point.

# Basic synopsis
alphajudge PATH [PATH ...] \
  --models_to_analyse {best,all} \
  --contact_thresh 8.0 \
  --pae_filter 100.0 \
  [-r|--recursive] \
  [-o|--summary SUMMARY.csv]

• PATH: One or more run directories or roots to search
• --contact_thresh: Contact cutoff in Å (default: 8.0)
• --pae_filter: Skip interfaces with avg interface PAE above this (default: 100.0)
• --models_to_analyse: best or all (default: best)
• -r / --recursive: Recursively discover runs under each PATH
• -o / --summary: Write an aggregated CSV across all processed runs

Outputs:

• Always writes interfaces.csv inside each processed run directory.
• For each processed model, also writes a PAE heatmap PNG pae_<model>.png next to interfaces.csv.
• If --summary is provided, also writes a union-header CSV at the given path containing rows from all runs.

Examples

# Single AF2 run (directory contains ranking_debug.json, pae_*.json, and model files)
alphajudge test_data/af2/pos_dimers/Q13148+Q92900

# Single AF3 run (directory contains ranking_scores.csv, per-model summary/confidence files, and model files)
alphajudge test_data/af3/pos_dimers/Q13148+Q92900 --models_to_analyse all

# Aggregate multiple runs into one summary
alphajudge test_data/af2/pos_dimers/Q13148+Q92900 \
           test_data/af3/pos_dimers/Q13148+Q92900 \
           -o interfaces_summary.csv

# Recursively discover runs under roots and write a combined summary
alphajudge test_data/af2/pos_dimers test_data/af3/pos_dimers -r -o interfaces_summary.csv

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Programmatic use

Minimal example:

from pathlib import Path
from alphajudge.parsers import pick_parser
from alphajudge.runner import process, process_many

run_dir = Path("test_data/af2/pos_dimers/Q13148+Q92900")
parser = pick_parser(run_dir)
print("Detected parser:", parser.name)  # "af2" or "af3"
process(str(run_dir), contact_thresh=8.0, pae_filter=100.0, models_to_analyse="best")
print("Wrote:", run_dir / "interfaces.csv")

# Multiple runs + optional recursion and summary
process_many(
    [str(run_dir), "test_data/af3/pos_dimers/Q13148+Q92900"],
    contact_thresh=8.0,
    pae_filter=100.0,
    models_to_analyse="best",
    recursive=False,
    summary_csv="interfaces_summary.csv",
)

Key outputs per interface include: average_interface_pae, interface_average_plddt, interface_contact_pairs, interface_area, interface_hb, interface_sb, interface_sc, interface_solv_en, interface_ipSAE, interface_LIS, interface_pDockQ2, and per-run pDockQ/mpDockQ.

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Expected input layout

AlphaJudge expects standard AlphaFold run outputs.

• AF2: directory with ranking_debug.json, pae_<model>.json, and model structure files (model.cif or *.pdb/*.cif)
• AF3: directory with ranking_scores.csv, per-model summary_confidences.json and confidences.json (or top-level ranked_0_summary_confidences.json), and structure files

The tool searches for model.cif inside each model subdirectory first; otherwise it tries to match *<model>*.cif or *<model>*.pdb at the run root.

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Output schema (CSV)

AlphaJudge writes interfaces.csv with one row per interface (and includes the selected model). Core fields include:

• jobs: run directory name
• model_used: selected model identifier
• interface: chain-pair label (e.g., A_B)
• iptm_ptm, iptm, ptm, confidence_score: unified AF confidences
• pDockQ/mpDockQ: global dockQ-like score (mpDockQ if multimer; pDockQ if dimer)
• average_interface_pae, interface_average_plddt, interface_num_intf_residues
• interface_contact_pairs, interface_score, interface_pDockQ2, interface_ipSAE, interface_LIS
• interface_hb, interface_sb, interface_sc, interface_area, interface_solv_en

Exact header is asserted in tests to be consistent across AF2 and AF3 runs.

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Testing

pytest -q

Tests exercise both AF2 and AF3 parsers and validate the CSV fields against bundled fixtures in test_data/.

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Docker

A minimal multi-stage Dockerfile is provided under docker/:

# Build image (runs tests in the build stage)
docker build -t alphajudge -f docker/Dockerfile .

# Inspect CLI inside the runtime image
docker run --rm alphajudge alphajudge --help

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Citation and license

Please cite:

AlphaJudge: we will come up with a better name. (xxxx). https://github.com/KosinskiLab/AlphaJudge

License: MIT for this repository. AlphaFold2/AlphaFold3, and other tools remain under their own licenses.

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