DeepRPI

Deep learning-based RNA-protein interaction prediction tool, official model for dry lab of iGEM 2025 PekingHSC.

Project Overview

DeepRPI is a deep learning-based RNA-protein interaction prediction tool. It leverages pretrained protein language models (ESM) and RNA language models (RNABert) to generate sequence embeddings, captures complex relationships between protein and RNA sequences through a bidirectional cross-attention mechanism, and ultimately uses a multilayer perceptron for interaction prediction.

Key Features

• Uses state-of-the-art pretrained language models for sequence embedding
• Implements bidirectional cross-attention mechanism to capture protein-RNA interaction patterns
• Generates attention heatmaps to visualize key regions of interaction
• Provides complete training, evaluation, and prediction pipeline

Project Structure

DeepRPI/
├── deeprpi/                      # Core modules
│   ├── model/                    # Model-related code
│   │   ├── attention.py          # Cross-attention mechanism implementation
│   │   ├── classifier.py         # Classifier model
│   │   └── embedding.py          # Embedding generation modules
│   ├── utils/                    # Utility functions
│   │   ├── data.py               # Data processing module
│   │   ├── evaluation.py         # Model evaluation module
│   │   ├── lightning_modules.py  # PyTorch Lightning modules
│   │   ├── prediction.py         # Prediction functionality
│   │   └── trainer.py            # Training functionality
│   └── config/                   # Configuration files
├── data/                         # Data directory
├── train.py                      # Training entry script
├── predict.py                    # Prediction entry script
└── evaluate.py                   # Evaluation entry script

Installation

1. Clone the repository:

2. Install dependencies:

pip install -r requirements.txt

Usage

Training a Model

python train.py --data_path data/your_data.csv --max_epochs 20 --batch_size 16

Parameters:

• --data_path: Path to training data file
• --batch_size: Training batch size
• --max_epochs: Maximum number of training epochs
• --hidden_dim: Hidden layer dimension
• --dropout: Dropout rate

Predicting Interactions

python predict.py --protein "MQVDPRLGRTLGKLGKLGRTGRNPQTDKPSLQ" --rna "ACCCGUGUGGUAGCGCAUUAUCGCGCUCACGC"

Parameters:

• --protein: Protein sequence (amino acid sequence)
• --rna: RNA sequence (nucleotide sequence)
• --checkpoint: Model checkpoint path (optional, uses pretrained model by default)
• --output_dir: Output directory
• --plot_attention: Whether to generate attention heatmaps

Evaluating Model Performance

python evaluate.py --data_path data/test_data.csv --checkpoint models/best_model.ckpt

Parameters:

• --data_path: Path to data file
• --checkpoint: Model checkpoint path
• --output_dir: Output directory
• --save_attention: Whether to save attention heatmaps
• --eval_val: Whether to evaluate validation set (evaluates test set by default)

Model Architecture

DeepRPI uses the following components:

1. Sequence Embedding:

   • Protein sequence: Generated using ESM-2 pretrained model
   • RNA sequence: Generated using RNABert pretrained model

2. Cross-Attention Mechanism: Implements bidirectional protein-RNA attention calculation to capture interaction patterns

3. Classifier: Uses a multilayer perceptron for binary classification prediction

Citation

If you use DeepRPI in your research, please cite our work:

@article{DeepRPI2025,
  title={DeepRPI: Deep learning-based RNA-protein interaction prediction with cross-attention mechanism},
  author={PekingHSC-iGEM Team},
  year={2025}
}