raep 1.0.0

Random Forest Enzyme Prediction

RAEP: Rapid Enzyme/Non-Enzyme Prediction

RAEP (Rapid Enzyme/Non-Enzyme Prediction) is an efficient enzyme/non-enzyme prediction tool for protein sequences. It is built on multi-physicochemical property features and the XGBoost machine learning algorithm.

🚀 Features

• Efficient Prediction: Achieves fast and accurate enzyme/non-enzyme classification using optimized feature extraction and the XGBoost model.
• Multi-Mode Support: Supports single-sequence prediction, multi-sequence batch prediction, and FASTA file batch prediction.
• Rich Feature Set: Utilizes multi-physicochemical property pseudo-amino acid composition (Pseudo-AAC), CTD features, and windowed amino acid composition.
• User-Friendly: Offers a concise Python API that is easy to integrate into existing projects.
• Multi-Process Optimization: Employs multi-process parallel processing in the feature extraction step to improve processing efficiency for large-scale datasets.

📦 Dependencies

• joblib: Used for model saving/loading and parallel processing.

• numpy: Numerical computation.

• pandas: Data processing.

• scikit-learn: Machine learning utilities and evaluation metrics.

• xgboost: Implementation of the gradient boosting tree algorithm.

  Requirements: Python 3.7 or higher.

📥 Installation

Install from PyPI (Recommended)

pip install raep

💻 Basic Usage

Import and Initialization

from raep import RAEP

# Default initialization (uses built-in model)
predictor = RAEP()

# Initialization with a custom model path (optional)
# predictor = RAEP(model_path="path/to/your/model.pkl")

Quick start with CLI

raep --input /your_fasta/file.fasta --output /path_to_your_result/result.json

Single Sequence Prediction

# Predict a single protein sequence
sequence = "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR"

prediction = predictor.predict(sequence)
probability = predictor.predict_proba(sequence)

print(f"Prediction result: {'Enzyme' if prediction == 1 else 'Non-Enzyme'}")
print(f"Prediction probabilities: Non-Enzyme={probability[0]:.4f}, Enzyme={probability[1]:.4f}")

Batch Prediction from FASTA Files

# Batch predict from a FASTA file
fasta_path = "test_sequences.fasta"
results = predictor.predict_fasta(fasta_path)

print(f"Prediction results ({len(results)} sequences):")
for i, (pred, prob) in enumerate(results, 1):
    print(f"Sequence {i}: {'Enzyme' if pred == 1 else 'Non-Enzyme'} (Enzyme probability: {prob[1]:.4f})")

📚 API Reference

RAEP Class

Initialization

RAEP(model_path=None)

• Purpose: Instantiates the RAEP predictor, automatically loads the model and initializes feature extraction parameters (e.g., LAG=10, W=0.05), ensuring consistency in subsequent prediction workflows.
• Parameters:
  • model_path: Optional. Path to a custom model file. If not provided, the built-in enzyme_xgb_model.pkl model will be used.

Methods

predict(sequence)

• Purpose: Predicts whether a single protein sequence is an enzyme.
• Parameters:
  • sequence (String): The protein sequence to be predicted.
• Returns:
  • prediction (Int): 0 = Non-enzyme, 1 = Enzyme.

predict_proba(sequence)

• Purpose: Predicts whether a single protein sequence is an enzyme.
• Parameters:
  • sequence (String): The protein sequence to be predicted.
• Returns:
  • probability (float): Probability of the sequence being an enzyme.

predict_fasta(fasta_path)

• Purpose: Performs batch prediction for protein sequences from a FASTA file.
• Parameters:
  • fasta_path (String): Path to the FASTA file.
• Returns: List of (prediction, probability) tuples corresponding to the sequences in the file.

📝 Notes

1. Sequence Format Requirements: Input sequences should only contain single-letter codes (uppercase) for the 20 standard amino acids.
2. Sequence Length: The tool automatically processes sequences of different lengths, but excessively short sequences (e.g., < 10 amino acids) may affect prediction accuracy.
3. Multi-Process Processing: The feature extraction process uses multi-processing acceleration by default, which automatically adjusts based on the number of CPU cores in the system.
4. Model File: Ensure the model file exists and is accessible, especially when using a custom model path.

🔧 Troubleshooting

• Failed to import the RAEP package: Ensure the package is correctly installed in the current Python environment (pip show raep).
• Model loading failure: Verify that the model file path is correct and the file exists at the specified location.
• Prediction errors: Check if the input sequence format is valid and contains only standard amino acid characters.
• Performance issues: For extremely large datasets, consider processing in batches to avoid memory overflow.

🤝 Getting Help

If you encounter any problems, please contact the author:

• Author: DHY
• Email: [e-mail]

📄 License

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

🙏 Acknowledgments

The development of this project is supported by several open-source tools, especially machine learning libraries such as XGBoost and scikit-learn.

---

Version: 1.0.0 | Last updated: 2025