AI Protein Design Pipeline

An end-to-end AI-powered pipeline for therapeutic protein design, from data collection to final candidate selection.

Overview

This project implements a comprehensive pipeline for AI-assisted protein design that:

1. Collects raw protein data from multiple sources (UniProt, PDB, AlphaFold DB)
2. Preprocesses and cleans the data for analysis
3. Generates novel protein sequences using AI models
4. Predicts 3D structures using state-of-the-art methods
5. Analyzes protein-target docking interactions
6. Assesses protein stability and dynamics
7. Evaluates developability and drug-like properties
8. Generates comprehensive reports and visualizations

Features

• Modular Design: Each pipeline stage is independent and configurable
• Multiple AI Models: Support for ProGen, ESMFold, AlphaFold, and other tools
• Comprehensive Analysis: From sequence to structure to function to developability
• Visualization: Rich plots and reports for all pipeline stages
• Configurable: YAML-based configuration for easy customization
• Extensible: Easy to add new models, analysis methods, or data sources

Project Structure

ai_protein_design_project/
├── config.yaml                    # Main configuration file
├── target_spec.yaml              # Target protein specifications
├── requirements.txt              # Python dependencies
├── README.md                     # This file
├── scripts/                      # Pipeline modules
│   ├── __init__.py
│   ├── pipeline.py              # Main pipeline orchestrator
│   ├── data_collection.py       # Data collection from databases
│   ├── data_preprocessing.py    # Data cleaning and preprocessing
│   ├── sequence_generation.py   # AI sequence generation
│   ├── structure_prediction.py  # 3D structure prediction
│   ├── docking.py               # Protein-target docking
│   ├── stability_analysis.py    # Stability assessment
│   ├── developability.py        # Developability evaluation
│   └── reporting.py             # Report generation
├── data/                        # Data directory
│   ├── raw/                     # Raw downloaded data
│   └── processed/               # Cleaned and processed data
├── models/                      # Model files and configurations
├── results/                     # Pipeline outputs
│   ├── sequences/               # Generated sequences
│   ├── structures/              # Predicted structures
│   ├── docking/                 # Docking results
│   ├── md/                      # Stability analysis
│   └── reports/                 # Final reports and plots
└── notebooks/                   # Jupyter notebooks (optional)

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• Git (for version control)
• CUDA-compatible GPU (optional, for faster deep learning training)

Setup

1. Clone or download the project files to your local machine

2. Install dependencies:

   pip install -r requirements.txt

   For GPU support (recommended for deep learning):

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   pip install fair-esm

3. Configure the pipeline by editing config.yaml and target_spec.yaml:

   • Set your target protein (UniProt ID, PDB ID, etc.)
   • Configure model parameters
   • Set file paths and computational resources

Deep Learning Models

This pipeline now includes proper deep learning implementations:

Sequence Generation Model (ProteinVAE)

• Architecture: Variational Autoencoder with LSTM encoder/decoder
• Input: Protein sequences (FASTA format)
• Output: Novel protein sequences with learned properties
• Training: Unsupervised learning on protein sequence datasets
• Fine-tuning: Optional fine-tuning on specific motifs or properties

Structure Prediction Model (ESMFold Integration)

• Model: Facebook's ESMFold (Evolutionary Scale Modeling)
• Input: Protein sequences
• Output: 3D structures (PDB format) with confidence scores (pLDDT, PAE)
• Features: End-to-end structure prediction without MSA

Dataset Creation

The pipeline automatically creates deep learning datasets:

• Tensor datasets for PyTorch training
• Train/validation splits for model evaluation
• Sequence encoding with amino acid vocabulary
• DataLoader creation for efficient batch processing

Configuration

Main Configuration (config.yaml)

project:
  name: "AI Protein Design Pipeline"
  version: "1.0.0"
  description: "End-to-end AI-powered therapeutic protein design"

# File paths and data sources
paths:
  data:
    raw: "data/raw"
    processed: "data/processed"
  results:
    sequences: "results/sequences"
    structures: "results/structures"
    docking: "results/docking"
    md: "results/md"
    reports: "results/reports"

# Model configurations
models:
  sequence_generator:
    name: "ProGen"  # or ESM, ProteinGAN
    max_length: 512
  structure_predictor:
    name: "ESMFold"  # or AlphaFold2
    device: "cpu"

Target Specification (target_spec.yaml)

target_protein:
  name: "HER2_binder"
  uniprot_id: "P04626"
  pdb_id: "1N8Z"
  alphafold_id: "P04626"

design_goals:
  type: "binder"
  function: "therapeutic_inhibitor"

success_criteria:
  structure_quality:
    plddt_threshold: 75.0
  binding_affinity:
    docking_energy_threshold: -7.5
  stability:
    rmsd_threshold: 2.0

Usage

Running the Complete Pipeline

# From the project root directory
python scripts/pipeline.py

Running Individual Stages

# Run specific stages
python scripts/pipeline.py --start-stage data_collection --end-stage structure_prediction

# Available stages:
# - data_collection
# - data_preprocessing
# - sequence_generation
# - structure_prediction
# - docking_analysis
# - stability_analysis
# - developability_assessment
# - reporting

Training Deep Learning Models

# Train the sequence generation model
python scripts/train_model.py --epochs 10 --num_sequences 100

# Fine-tune existing model
python scripts/train_model.py --skip_training --finetune_epochs 5

# Generate sequences only (requires trained model)
python scripts/train_model.py --skip_training --num_sequences 50

Using ESMFold for Structure Prediction

# The pipeline automatically uses ESMFold when available
# To check if ESMFold is installed:
python -c "import esm; print('ESMFold available')"

Manual Model Training

from scripts.sequence_generation import SequenceGenerator, ProteinVAE
from scripts.data_preprocessing import DataPreprocessor

# Load data
preprocessor = DataPreprocessor(config, target_spec)
sequences = preprocessor.prepare_training_data()

# Train model
generator = SequenceGenerator(config, target_spec)
generator.train_model(sequences, epochs=10)

# Generate sequences
results = generator.generate()

Checking Pipeline Status

python scripts/pipeline.py --status

Pipeline Stages

1. Data Collection

• Downloads protein sequences from UniProt
• Retrieves structural data from PDB
• Gets AlphaFold predictions from AlphaFold DB
• Collects related sequences for reference

2. Data Preprocessing

• Filters sequences by length and quality
• Removes duplicates and low-quality entries
• Calculates physicochemical properties
• Standardizes data formats

3. Sequence Generation

• Uses AI models (ProGen, ESM) to generate novel sequences
• Applies template-based or de novo generation
• Filters by desired properties
• Ensures diversity in generated sequences

4. Structure Prediction

• Predicts 3D structures using ESMFold or AlphaFold
• Evaluates confidence scores (pLDDT, PAE)
• Filters structures by quality thresholds
• Outputs PDB files for further analysis

5. Docking Analysis

• Prepares target and ligand structures
• Runs molecular docking simulations
• Calculates binding energies and poses
• Identifies promising candidates

6. Stability Analysis

• Performs molecular dynamics simulations
• Calculates stability metrics (RMSD, energy)
• Uses proxy methods for quick assessment
• Evaluates conformational stability

7. Developability Assessment

• Assesses solubility and aggregation risk
• Evaluates immunogenicity potential
• Checks manufacturability
• Provides overall developability score

8. Reporting

• Generates comprehensive text reports
• Creates visualizations for all stages
• Summarizes key findings and recommendations
• Exports results in multiple formats

Output Files

The pipeline generates several output files in the results/ directory:

• Sequences: results/sequences/generated_sequences.fasta
• Structures: results/structures/*_esmfold.pdb (or alphafold)
• Docking: results/docking/docking_results.csv, results/docking/top_binders.csv
• Stability: results/md/stability_results.csv, results/md/stable_candidates.csv
• Reports: results/reports/pipeline_report.txt, results/reports/pipeline_summary.yaml
• Visualizations: Multiple PNG files with plots and charts

Example Output

After running the pipeline, you should see:

results/
├── sequences/
│   ├── generated_sequences.fasta
│   └── sequence_properties.csv
├── structures/
│   ├── GEN_0001_esmfold.pdb
│   └── structure_confidence_scores.csv
├── docking/
│   ├── docking_results.csv
│   └── top_binders.csv
├── md/
│   ├── stability_results.csv
│   └── stable_candidates.csv
└── reports/
    ├── pipeline_report.txt
    ├── pipeline_summary.yaml
    ├── pipeline_overview.png
    ├── sequence_properties.png
    ├── structure_confidence.png
    ├── docking_results.png
    ├── stability_results.png
    └── developability_results.png

Customization

Adding New Models

To add a new sequence generation model:

1. Create a new method in sequence_generation.py
2. Update the model configuration in config.yaml
3. Modify the generate() method to use your model

Modifying Analysis Criteria

Edit target_spec.yaml to adjust:

• Quality thresholds (pLDDT, binding energy, RMSD)
• Sequence length ranges
• Developability criteria
• Success thresholds

Adding New Data Sources

1. Update data_sources in config.yaml
2. Add collection methods in data_collection.py
3. Update the main collection workflow

Computational Requirements

Minimum Requirements

• Python 3.8+
• 8 GB RAM
• 10 GB disk space
• Internet connection (for data download)

Recommended for Full Pipeline

• Python 3.10+
• 16+ GB RAM
• 50+ GB disk space
• CUDA-compatible GPU (optional, for faster structure prediction)

Large Dataset Processing

• 32+ GB RAM
• High-performance CPU
• SSD storage
• GPU cluster (recommended for AlphaFold)

Troubleshooting

Common Issues

1. Memory Errors

   • Reduce batch sizes in configuration
   • Process sequences in smaller chunks
   • Use CPU-only modes for large datasets

2. Missing Dependencies

   pip install -r requirements.txt
   # Install additional tools as needed:
   # conda install -c conda-forge autodock vina
   # conda install -c conda-forge gromacs

3. Data Download Failures

   • Check internet connection
   • Verify API endpoints in configuration
   • Some databases may have access restrictions

4. Structure Prediction Issues

   • Ensure sufficient disk space for PDB files
   • Check model download and installation
   • Use CPU mode if GPU memory is insufficient

Getting Help

• Check the log files in the project directory
• Review the configuration files for errors
• Ensure all required tools are installed
• Check the issues section in the project repository

Citation

If you use this pipeline in your research, please cite:

AI Protein Design Pipeline v1.0.0
[Your Institution]
[Year]

License

This project is provided for research and educational purposes. Please check individual tool licenses for specific usage restrictions.

Contributing

Contributions are welcome! Areas for improvement:

• Additional AI models for sequence generation
• More structure prediction methods
• Enhanced docking algorithms
• Advanced visualization features
• Parallel processing capabilities
• Cloud deployment options

Version History

• v1.0.0: Initial release with complete pipeline
• Modular design with 8 distinct stages
• Support for major bioinformatics tools
• Comprehensive reporting and visualization

Contact

For questions, issues, or contributions, please contact the development team.

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This pipeline represents a complete solution for AI-assisted protein design, from concept to candidate molecules ready for experimental validation.