deployment_guide.md

AbMelt HF Space Deployment Guide

Complete Implementation Status ✅

This implementation provides a FULLY FUNCTIONAL molecular dynamics pipeline in Hugging Face Space with the following capabilities:

✅ Complete Pipeline Components

1. Structure Generation: ImmuneBuilder integration for antibody Fv generation
2. MD System Preparation: Complete GROMACS workflow (pdb2gmx, solvation, ionization)
3. Multi-temperature Simulations: Full MD at 300K, 350K, 400K with proper equilibration
4. Descriptor Calculation: Comprehensive analysis using GROMACS tools + MDAnalysis
5. ML Predictions: Integration of pre-trained Random Forest models for all targets

✅ Key Features

• Real MD Simulations: Not just predictions from pre-calculated data
• Progress Tracking: Real-time updates during long-running simulations
• Resource Management: Intelligent queuing and memory management for HF Space
• Error Recovery: Robust error handling and cleanup
• File Downloads: Access to intermediate files (structures, trajectories, descriptors)

Deployment Instructions

1. Pre-deployment Testing

# Test the pipeline locally
python test_pipeline.py

# Expected output: All tests should pass
# ✓ structure_generation: PASS
# ✓ gromacs_installation: PASS  
# ✓ ml_models: PASS
# ✓ quick_pipeline: PASS

2. Hugging Face Space Configuration

Create a new HF Space with these settings:

• Space Type: Gradio
• SDK Version: 4.44.0
• Hardware: CPU Upgrade (recommended for MD simulations)
• Persistent Storage: Enable for temporary files

3. Required Files for Deployment

Copy these files to your HF Space repository:

├── app.py                 # Main Gradio application
├── requirements.txt       # Python dependencies
├── packages.txt          # System packages (GROMACS)
├── Dockerfile            # Container configuration  
├── README.md             # Documentation
├── metadata.json         # HF Space metadata
├── src/                  # Source code modules
│   ├── structure_generator.py
│   ├── gromacs_pipeline.py
│   ├── descriptor_calculator.py
│   ├── ml_predictor.py
│   └── resource_manager.py
└── models/               # Pre-trained ML models
    ├── tagg/
    ├── tm/
    └── tmon/

4. Environment Variables (Optional)

Set these in HF Space settings if needed:

GRADIO_SERVER_NAME=0.0.0.0
GRADIO_SERVER_PORT=7860
PYTHONPATH=/app/src

5. Hardware Requirements

Minimum Requirements:

• CPU: 4 cores
• RAM: 8GB
• Disk: 20GB
• Time: 2-4 hours per antibody

Recommended for Production:

• CPU Upgrade (8 cores)
• RAM: 16GB
• Disk: 50GB
• Concurrent Users: 1-2 (due to MD simulation intensity)

Usage Instructions

Input Requirements

1. Heavy Chain Variable Region: Complete VH sequence (typically 110-130 residues)
2. Light Chain Variable Region: Complete VL sequence (typically 100-110 residues)
3. Simulation Parameters: Time (10-100ns) and temperatures (300,350,400K)

Expected Runtime

• Quick Test (10ns): ~30-60 minutes
• Standard Run (50ns): ~2-3 hours
• Full Run (100ns): ~4-6 hours

Output Files

Users can download:

• Generated Structure (PDB format)
• MD Trajectories (XTC format, compressed)
• Calculated Descriptors (CSV format)
• Predictions Summary (JSON format)

Implementation Highlights

🔬 Complete MD Workflow

The pipeline executes every step of the AbMelt protocol:

1. Structure Generation:

   # Uses ImmuneBuilder for Fv prediction
   structure_path = generator.generate_structure(heavy_chain, light_chain)
   

2. System Preparation:

   # Complete GROMACS preparation
   prepared_system = md_pipeline.prepare_system(structure_path)
   # Includes: pdb2gmx, solvation, ionization, energy minimization
   

3. MD Simulations:

   # Multi-temperature simulations
   trajectories = md_pipeline.run_md_simulations([300, 350, 400], sim_time_ns)
   # Includes: NVT equilibration, NPT equilibration, production MD
   

4. Descriptor Calculation:

   # Comprehensive analysis
   descriptors = descriptor_calc.calculate_all_descriptors(trajectories, topology_files)
   # Includes: SASA, H-bonds, RMSF, Rg, order parameters
   

5. ML Predictions:

   # Use pre-trained models
   predictions = predictor.predict_thermostability(descriptors)
   # Returns: Tagg, Tm,on, Tm with confidence estimates
   

🛠️ Technical Architecture

• Modular Design: Separate classes for each pipeline component
• Error Handling: Comprehensive try-catch with informative messages
• Resource Management: Memory and disk usage monitoring
• Progress Tracking: Real-time updates via Gradio interface
• Cleanup: Automatic temporary file removal

🚦 Quality Assurance

• Input Validation: Sequence format and length checking
• Intermediate Verification: File existence and size validation
• Error Recovery: Graceful handling of GROMACS/ImmuneBuilder failures
• Resource Monitoring: Automatic cleanup of long-running jobs

Troubleshooting

Common Issues

1. GROMACS Not Found

   • Ensure packages.txt includes gromacs installation
   • Check Dockerfile has correct system dependencies

2. Memory Issues

   • Reduce simulation time for initial testing
   • Enable HF Space persistent storage
   • Monitor resource usage in logs

3. Long Queue Times

   • Pipeline limits to 1 concurrent user due to MD intensity
   • Consider upgrading to CPU+ hardware tier

4. ImmuneBuilder Errors

   • Validate input sequences are complete variable regions
   • Check for non-standard amino acid characters

Performance Optimization

• Simulation Length: Start with 10ns for testing, scale to 100ns for production
• Temperature Selection: Use default 300,350,400K for best model performance
• Hardware: CPU Upgrade significantly improves performance
• Queue Management: Implemented automatic job queuing and resource monitoring

Success Metrics

The deployment is successful when:

✅ Users can input antibody sequences
✅ Complete MD simulations run without errors
✅ All descriptors are calculated correctly
✅ ML models produce valid predictions
✅ Intermediate files are downloadable
✅ Pipeline completes within expected timeframes

This implementation delivers a fully functional research-grade molecular dynamics pipeline accessible through a user-friendly web interface, making advanced antibody thermostability prediction available to the broader scientific community.