README.md

metadata

license: cc-by-4.0
language:
  - en
tags:
  - mitochondrial-variants
  - pathogenicity-prediction
  - protein-language-model
  - ESM-2
  - machine-learning
  - bioinformatics
pretty_name: IDP Pathogenicity Model – Mitochondrial Missense Variants
size_categories:
  - 10K < n < 100K

IDP Pathogenicity Model

Mechanistically Informed Prediction of Pathogenicity for Mitochondrial Missense Variants

Harrizi S., Nait Irahal I., Mostafa K., Arnoult D. Submitted to Bioinformatics (2025)

This repository / model card describes a hybrid machine-learning classifier that predicts the pathogenicity of missense variants in mitochondrial proteins.

The model combines ESM-2 (t33_650M_UR50D) protein language model embeddings with 45 carefully designed biophysical and mitochondrial-specific features, and is trained under strict leave-protein-out cross-validation to minimize data leakage.

Key Performance (Leave-Protein-Out CV)

Model	AUC–ROC	AUC–PR
Our model (MLP + ESM-2 + features)	0.899	0.923
Random Forest + ESM-2	0.882	—
Logistic Regression + ESM-2	0.864	—
AlphaMissense	0.942	0.954
PolyPhen-2	0.845	0.799
SIFT	0.826	0.749

All tools evaluated on the same ClinVar mitochondrial missense set under leave-protein-out protocol.

Model Inputs

• ESM-2 embeddings — global protein embedding + local window (±15 residues)
• 45 biophysical features — conservation, disorder (DisProt/MobiDB), AlphaFold pLDDT, charge changes, hydrophobicity, mitochondrial-specific annotations, etc.

Final input dimensionality after PCA: 173 features (128 PCA-reduced ESM dimensions + 45 classical features)

Architecture

Multi-Layer Perceptron (MLP)

Layer	Units	Activation	Regularization
Input	173	—	—
Hidden 1	256	ReLU	Dropout 0.3
Hidden 2	128	ReLU	Dropout 0.2
Output	1	Sigmoid	—

• Optimizer: Adam (lr=0.001, weight decay=1e-4)
• Loss: Binary Cross-Entropy
• Preprocessing: StandardScaler + PCA (fitted only on train fold)
• Training epochs: 30–50 (early stopping used)

Dataset Summary

• Total variants: 11,928 ClinVar missense variants (release 2024/12)
• Pathogenic / likely pathogenic: 6,882 (57.7%)
• Benign / likely benign: 5,046 (42.3%)
• Mitochondrial proteins: 639 (MitoCarta 3.0 + OMIM filtering)
• Sequence source: UniProt 2024_05
• Frozen & MD5-versioned dataset available in data/frozen/

Repository Structure (main folders)

IDP-Pathogenicity-Model/
├── data/               # raw, processed & frozen datasets
├── embeddings/         # precomputed ESM-2 embeddings (.npy, .pkl)
├── features/           # extracted biophysical features
├── models/             # trained checkpoints (joblib/pkl)
├── results/            # LPOCV predictions, feature importances, etc.
├── scripts/            # full reproducible pipeline
└── configs/            # YAML configuration files

Quick Start – Inference

Once you have the model checkpoint and either precomputed embeddings or an ESM-2 inference setup, you can obtain pathogenicity predictions like this:

import joblib
import torch
import numpy as np
from fair_esm import pretrained

# Load the trained classifier
model = joblib.load("models/pathogenicity_classifier_v2.pkl")            # adjust path if necessary

# (Optional) Load ESM-2 if you need to compute embeddings yourself
esm_model, alphabet = pretrained.load_model_and_alphabet("esm2_t33_650M_UR50D")
esm_model = esm_model.eval()
if torch.cuda.is_available():
    esm_model = esm_model.cuda()

# ───────────────────────────────────────────────────────────────
# Example: predict for one variant
# ───────────────────────────────────────────────────────────────
# You must provide a (1, 173) array containing:
#   • 128 PCA-reduced ESM-2 embedding dimensions (global + local ±15 residues)
#   • 45 biophysical / mitochondrial-specific features
#
# (Full feature-extraction + inference pipeline coming soon)

features = np.random.rand(1, 173).astype(np.float32)   # ← REPLACE with your real features!

prob_pathogenic = model.predict_proba(features)[0, 1]
print(f"Predicted probability of being pathogenic: {prob_pathogenic:.4f}")

A complete end-to-end inference script (from protein sequence + variant → ESM embedding → features → final prediction) is in preparation and will be added shortly.

Reproducing the Experiments

Run the scripts in this order (all located in the scripts/ folder):

1. data_download.py
2. build_clinvar_mito_dataset.py + build_mutation_dataset.py
3. phase1_freeze_and_classical_features.py
4. esm2_t33_650M_UR50D.py (GPU recommended – ~2–4 hours runtime)
5. final_mlp_embedding_model.py + lpocv_validation.py

Important Notes & Limitations

• The model has been specifically developed and optimized for missense variants in mitochondrial proteins — performance on nuclear-encoded proteins has not been assessed.
• AlphaMissense achieves higher AUC on broader variant sets (0.942 vs our 0.899), but our model remains very competitive while using far fewer parameters and explicitly incorporating mitochondrial biology knowledge.
• All performance metrics are derived from strict leave-protein-out cross-validation, giving a realistic estimate of generalization to completely unseen proteins.

Citation

@article{harrizi2025mechanistically,
  title   = {Mechanistically informed machine learning for pathogenicity prediction of mitochondrial missense variants},
  author  = {Harrizi, Saad and Nait Irahal, Imane and Mostafa, Kabine and Arnoult, Damien},
  journal = {Bioinformatics},
  year    = {2025},
  note    = {Submitted}
}

Authors & Contact

• Damien Arnoult (corresponding author) — INSERM UMR-S-MD 1193 & UMR-S 1124, Paris
  ✉ damien.arnoult@inserm.fr
• Saad Harrizi (main developer) — Université Hassan II de Casablanca
  ✉ saadharrizi0@gmail.com

We welcome any feedback, questions, or collaboration proposals!