raep 0.0.4

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, 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.

Installation Methods

Install from PyPI (Recommended)

pip install raep

Install from Source Code

# 克隆仓库（如果有）
git clone <repository-url>
cd RAEP

# 开发模式安装
pip install -e .

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.

Basic Usage

Import and Initialization

from raep_package import RAEP

# 默认初始化（使用内置模型）
predictor = RAEP()

# 使用自定义模型路径初始化
# predictor = RAEP(model_path="path/to/your/model.pkl")

Single Sequence Prediction

# 预测单个蛋白质序列
sequence = "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR"
prediction, probability = predictor.predict_sequence(sequence)

print(f"预测结果: {'酶' if prediction == 1 else '非酶'}")
print(f"预测概率: 非酶={probability[0]:.4f}, 酶={probability[1]:.4f}")

Batch Prediction for Multiple Sequences

# 批量预测多个序列
sequences = [
    "MASMTGGQQMGRGSEF",
    "MKVLVVLVLLAVLVLA",
    "MDEKTTGWRGGHVA"
]

results = predictor.predict_sequences(sequences)

for i, (pred, prob) in enumerate(results, 1):
    print(f"序列 {i}: {'酶' if pred == 1 else '非酶'} (酶概率: {prob[1]:.4f})")

Batch Prediction for FASTA Files

# 从FASTA文件批量预测
fasta_path = "test_sequences.fasta"
results = predictor.predict_fasta(fasta_path)

print(f"预测结果 ({len(results)} 个序列):")
for i, (pred, prob) in enumerate(results, 1):
    print(f"序列 {i}: {'酶' if pred == 1 else '非酶'} (酶概率: {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

predict_sequence(sequence)

Purpose : Predicts whether a single protein sequence is an enzyme.

• Parameters:

  • sequence: String, the protein sequence to be predicted.

• Return Value:

  • Tuple (prediction, probability):
    • prediction: Integer, the predicted class (0 = Non-enzyme, 1 = Enzyme).
    • probability:List ,containing two floats, representing the probabilities of the sequence being a non-enzyme and an enzyme, respectively.

predict_sequences

predict_sequences(sequences)

Purpose : Performs batch prediction for multiple protein sequences.

• Parameters :

  • sequences: List containing multiple protein sequence strings.

• Return Value:

  • List where each element is a tuple (prediction, probability) corresponding to the prediction result of the input sequence.

predict_fasta

predict_fasta(fasta_path)

Purpose : Performs batch prediction for protein sequences from a FASTA file.

• Parameters:

  • fasta_path: String, path to the FASTA file.

• Return Value:

  • List where each element is a tuple (prediction, probability) corresponding to the prediction result of the sequences in the FASTA file.

Usage Examples

Refer to the example_usage.py file in the project, which contains complete usage examples:

python example_usage.py

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

Common Issues

1. Failed to import the RAEP package: Ensure the package is correctly installed in the current Python environment.
2. Model loading failure: Verify that the model file path is correct and the file exists at the specified location.
3. Prediction errors: Check if the input sequence format is valid and contains only standard amino acid characters.
4. 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: 0.0.4 Last updated: 2025