amylodeep 0.2.8

Prediction of amyloid propensity from amino acid sequences using ensemble deep learning and LLM models

AmyloDeep

AmyloDeep: pLM-based ensemble model for predicting amyloid propensity from the amino acid sequence

AmyloDeep is a Python package that uses ensemble model to predict amyloidogenic regions in protein sequences using a rolling window approach.

Features

• Multi-model ensemble: Combines 5 different models for robust predictions
• Rolling window analysis: Analyzes sequences using sliding windows of configurable size
• Pre-trained models: Uses models trained on amyloid sequence databases
• Calibrated probabilities: Includes probability calibration for better confidence estimates
• Easy-to-use API: Simple Python interface and command-line tool
• Streamlit web interface: Optional web interface for interactive predictions

Installation

From PyPI (recommended)

pip install amylodeep

From source

git clone https://github.com/AlisaDavtyan/protein_classification.git
cd amylodeep
pip install amylodeep

Quick Start

Python API

from amylodeep import predict_ensemble_rolling

# Predict amyloid propensity for a protein sequence
sequence = "MKTFFFLLLLFTIGFCYVQFSKLKLENLHFKDNSEGLKNGGLQRQLGLTLKFNSNSLHHTSNL"
result = predict_ensemble_rolling(sequence, window_size=6)

print(f"Average probability: {result['avg_probability']:.4f}")
print(f"Maximum probability: {result['max_probability']:.4f}")

# Access position-wise probabilities
for position, probability in result['position_probs']:
    print(f"Position {position}: {probability:.4f}")

Command Line Interface

# Basic prediction
amylodeep "MKTFFFLLLLFTIGFCYVQFSKLKLENLHFKDNSEGLKNGGLQRQLGLTLKFNSNSLHHTSNL"

# With custom window size
amylodeep "SEQUENCE" --window-size 10

# Save results to file
amylodeep "SEQUENCE" --output results.json --format json

# CSV output
amylodeep "SEQUENCE" --output results.csv --format csv

Model Architecture

AmyloDeep uses an ensemble of 5 models:

The models are combined using probability averaging, with some models using probability calibration (Platt scaling or isotonic regression) for better confidence estimates.

Requirements

• Python >= 3.8
• PyTorch >= 1.9.0
• Transformers >= 4.15.0
• NumPy >= 1.20.0
• scikit-learn >= 1.0.0
• XGBoost >= 1.5.0
• jax-unirep >= 2.0.0
• wandb >= 0.12.0

Main Functions

predict_ensemble_rolling(sequence, window_size=6)

Predict amyloid propensity for a protein sequence using rolling window analysis.

Parameters:

• sequence (str): Protein sequence (amino acid letters)
• window_size (int): Size of the rolling window (default: 6)

Returns: Dictionary containing:

• position_probs: List of (position, probability) tuples
• avg_probability: Average probability across all windows
• max_probability: Maximum probability across all windows
• sequence_length: Length of the input sequence
• num_windows: Number of windows analyzed

Individual model classes for ESM and UniRep-based predictions.

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use AmyloDeep in your research, please cite:

@software{amylodeep2025,
  title={AmyloDeep: Prediction of amyloid propensity from amino acid sequences using deep learning},
  author={Alisa Davtyan},
  year={2025},
  url={https://github.com/AlisaDavtyan/protein_classification}
}

Support

For questions and support:

• Open an issue on GitHub
• Contact: [e-mail]