# 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

```bash
# 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**: 
   ```python
   # Uses ImmuneBuilder for Fv prediction
   structure_path = generator.generate_structure(heavy_chain, light_chain)
   ```

2. **System Preparation**:
   ```python
   # Complete GROMACS preparation
   prepared_system = md_pipeline.prepare_system(structure_path)
   # Includes: pdb2gmx, solvation, ionization, energy minimization
   ```

3. **MD Simulations**:
   ```python
   # 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**:
   ```python
   # Comprehensive analysis
   descriptors = descriptor_calc.calculate_all_descriptors(trajectories, topology_files)
   # Includes: SASA, H-bonds, RMSF, Rg, order parameters
   ```

5. **ML Predictions**:
   ```python
   # 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.