🧬 Proteomorphic

Decoding Protein Misfolding at Scale

Advanced computational platform for protein structure analysis, misfolding prediction, and precision therapeutic intervention in neurodegenerative disorders

Features • Demo • Installation • Usage • Technology • Contributing

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📋 Table of Contents

• Overview
• Authors
• Features
• Demo
• Technology Stack
• Installation
• Usage
• Project Structure
• API Documentation
• Screenshots
• Roadmap
• Contributing
• License
• Acknowledgments

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🔬 Overview

Proteomorphic is a cutting-edge web-based platform that leverages artificial intelligence and computational biology to analyze protein structures, predict misfolding risks, and design therapeutic interventions for neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's disease.

The Problem

Protein misfolding is a critical factor in numerous neurodegenerative disorders. Traditional analysis methods are time-consuming, expensive, and require specialized expertise. There is a pressing need for accessible, automated tools that can:

• Rapidly analyze protein structures
• Identify misfolding hotspots
• Predict disease risk
• Design targeted therapeutic interventions

Our Solution

Proteomorphic provides an end-to-end automated pipeline that:

1. Analyzes protein sequences using AI-powered structure prediction (ESM-2)
2. Identifies misfolding hotspots and pathogenic mutations
3. Calculates misfolding risk scores based on biochemical properties
4. Designs CRISPR guide RNAs for therapeutic intervention
5. Generates comprehensive clinical reports with 3D visualizations
6. Monitors patient biomarkers through IoT integration

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👥 Authors

This project was developed by:

Author	GitHub	Role
bmsahana14	@bmsahana14	Lead Developer
02pasco	@02pasco	Core Developer
007sanju	@007sanju	Core Developer
1ep22ic003	@1ep22ic003	Core Developer

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✨ Features

🔍 Protein Structure Analysis

• 3D Structure Visualization: Interactive protein structure viewer using 3Dmol.js
• Secondary Structure Prediction: Alpha helix, beta sheet, and random coil composition
• AI-Powered Analysis: ESM-2 protein language model for advanced predictions
• Biochemical Properties: Instability index, hydrophobicity (GRAVY), and charge analysis

🧬 Misfolding Detection

• Hotspot Identification: Detects aggregation-prone regions and pathogenic mutations
• Risk Assessment: Calculates misfolding risk scores (0-100) with confidence levels
• Disease Association: Maps proteins to known neurodegenerative disorders
• Clinical Interpretation: Generates actionable clinical insights

✂️ CRISPR Therapeutic Design

• Automated Guide RNA Design: Generates optimized 20bp guide sequences
• PAM Site Identification: NGG motif detection for SpCas9
• Efficiency Scoring: Predicts editing efficiency based on GC content
• Off-Target Analysis: Evaluates specificity and potential off-target effects
• Delivery System Recommendations: AAV9-PHP.eB for CNS, LNP for systemic delivery

🌐 IoT Biosensor Integration

• Real-Time Monitoring: Live patient biomarker tracking
• Multi-Parameter Analysis: Temperature, heart rate, protein levels, oxidative stress
• Risk Correlation: Links biosensor data to misfolding risk
• Alert System: Automated notifications for critical values

📊 Comprehensive Reporting

• Clinical Reports: Detailed analysis with 3D visualizations
• Export Functionality: PDF generation for medical records
• Historical Tracking: Patient analysis history and trends
• Dashboard Analytics: Visual insights and statistics

🔐 Security & Compliance

• User Authentication: Secure login with Supabase integration
• Role-Based Access: Admin, researcher, and patient roles
• Data Privacy: HIPAA/GDPR compliant architecture
• Encrypted Storage: Secure data handling and transmission

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🎬 Demo

Live Analysis Pipeline

Protein Input → Structure Analysis → Risk Assessment → CRISPR Design → Report Generation
     🧬              🔍                  ⚠️                ✂️              📋

Example Analysis

Input: Alpha-Synuclein (SNCA) - Parkinson's Disease Protein

Output:

• Misfolding Risk: 87/100 (High Risk)
• Hotspots Detected: A53T, A30P mutations
• CRISPR Guides: 3 optimized guide RNAs designed
• Clinical Recommendation: Therapeutic intervention recommended

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🛠️ Technology Stack

Frontend

• HTML5/CSS3: Modern, responsive UI with glassmorphism design
• JavaScript (ES6+): Dynamic interactions and API integration
• 3Dmol.js: Interactive 3D protein structure visualization
• Google Fonts (Inter): Premium typography

Backend

• Python 3.8+: Core analysis engine
• Flask 3.0.0: RESTful API framework
• Flask-CORS: Cross-origin resource sharing

AI/ML

• ESM-2 (650M): Facebook's protein language model
• PyTorch: Deep learning framework
• Transformers: Hugging Face model integration
• NumPy/Pandas: Scientific computing

Database & Authentication

• Supabase: PostgreSQL database and authentication
• LocalStorage: Client-side data persistence

External APIs

• UniProt: Protein sequence retrieval
• NCBI: Genetic variant data
• AlphaFold: Structure prediction integration

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📦 Installation

Prerequisites

• Python 3.8 or higher
• Node.js (optional, for development tools)
• Modern web browser (Chrome, Firefox, Edge)
• Git

Step 1: Clone the Repository

git clone https://github.com/YOUR_USERNAME/proteomorphic.git
cd proteomorphic

Step 2: Set Up Python Backend

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Step 3: Configure Environment Variables

Create a .env file in the backend directory:

FLASK_APP=app.py
FLASK_ENV=development
SECRET_KEY=your_secret_key_here
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key

Step 4: Start the Backend Server

python app.py

The backend will start at http://localhost:5000

Step 5: Launch the Frontend

# Navigate back to project root
cd ..

# Option 1: Use Python's built-in server
python -m http.server 8000

# Option 2: Use VS Code Live Server extension
# Right-click index.html → "Open with Live Server"

The frontend will be available at http://localhost:8000

Step 6: Access the Application

Open your browser and navigate to:

• Frontend: http://localhost:8000
• Backend API: http://localhost:5000/api/health

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🚀 Usage

Quick Start

1. Sign Up: Create an account at /auth/signup.html
2. Login: Access your dashboard at /auth/login.html
3. Analyze Protein:
   • Navigate to Analysis → Input
   • Enter protein name (e.g., "Alpha-Synuclein")
   • Click "Analyze Protein"
4. View Report: Review comprehensive analysis with 3D visualization
5. Monitor IoT: Check real-time biosensor data (if available)

Example API Request

// Analyze a protein
const response = await fetch('http://localhost:5000/api/analyze', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    proteinName: 'Alpha-Synuclein',
    proteinSequence: 'MDVFMKGLSKAKEGVVAAAEKTKQGVAEAAGKTKEGVLYVGSKTKEGVVHGVATVAEKTKEQVTNVGGAVVTGVTAVAQKTVEGAGSIAAATGFVKKDQLGKNEEGAPQEGILEDMPVDPDNEAYEMPSEEGYQDYEPEA'
  })
});

const result = await response.json();
console.log(result);

Response Format

{
  "proteinId": "Alpha-Synuclein",
  "proteinName": "Alpha-Synuclein",
  "misfoldingRisk": 87,
  "riskLevel": "High Risk",
  "confidence": 94,
  "structure": {
    "alphaHelix": 35.2,
    "betaSheet": 28.7,
    "randomCoil": 36.1,
    "instabilityIndex": 42.3,
    "gravy": -0.234
  },
  "hotspots": [
    {
      "residue": "A53T",
      "position": 53,
      "severity": "high",
      "confidence": 0.94,
      "impact": "Accelerates fibril formation, Parkinson's disease"
    }
  ],
  "crisprDesign": {
    "gene": "SNCA",
    "guideRNAs": [...],
    "deliverySystem": "AAV9-PHP.eB",
    "successProbability": 89.5
  }
}

---

📁 Project Structure

proteomorphic/
├── index.html                 # Landing page
├── logo.png                   # Project logo
├── system_architecture.png    # Architecture diagram
├── .gitignore                 # Git ignore rules
│
├── auth/                      # Authentication pages
│   ├── login.html
│   ├── signup.html
│   └── verify.html
│
├── dashboard/                 # User dashboard
│   └── index.html
│
├── analysis/                  # Protein analysis
│   └── input.html
│
├── report/                    # Analysis reports
│   └── view.html
│
├── css/                       # Stylesheets
│   ├── variables.css          # CSS variables
│   ├── global.css             # Global styles
│   └── components.css         # Component styles
│
├── js/                        # JavaScript modules
│   ├── auth.js                # Authentication logic
│   ├── utils.js               # Utility functions
│   ├── chatbot.js             # AI chatbot
│   ├── report-data-loader.js  # Report data handling
│   └── notifications.js       # Notification system
│
├── backend/                   # Python backend
│   ├── app.py                 # Flask API server
│   ├── requirements.txt       # Python dependencies
│   └── .env                   # Environment variables
│
├── images/                    # Image assets
│   └── [various images]
│
├── iot-monitoring.html        # IoT dashboard
├── knowledge.html             # Knowledge center
├── chatbot-widget.html        # Chatbot interface
└── admin.html                 # Admin panel

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📡 API Documentation

Endpoints

POST /api/analyze

Analyze a protein sequence and predict misfolding risk.

Request Body:

{
  "proteinName": "string",
  "proteinSequence": "string (optional)"
}

Response: Complete analysis object with structure, hotspots, and CRISPR design.

GET /api/health

Check backend health status.

Response:

{
  "status": "healthy",
  "model_loaded": true,
  "device": "cpu"
}

---

📸 Screenshots

Landing Page

Analysis Dashboard

3D Protein Viewer

Clinical Report

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🗺️ Roadmap

Version 1.0 (Current)

• ✅ Protein structure analysis
• ✅ Misfolding risk prediction
• ✅ CRISPR guide RNA design
• ✅ IoT biosensor integration
• ✅ User authentication

Version 2.0 (Planned)

• AlphaFold integration for structure prediction
• Multi-protein complex analysis
• Drug-protein interaction modeling
• Mobile application (iOS/Android)
• Cloud deployment (AWS/Azure)

Version 3.0 (Future)

• Real-time collaborative analysis
• Machine learning model training interface
• Integration with electronic health records (EHR)
• Advanced visualization (VR/AR)

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🤝 Contributing

We welcome contributions from the community! Here's how you can help:

How to Contribute

1. Fork the repository
2. Create a feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 for Python code
• Use ESLint for JavaScript
• Write clear commit messages
• Add tests for new features
• Update documentation

Areas for Contribution

• 🐛 Bug fixes
• ✨ New features
• 📝 Documentation improvements
• 🎨 UI/UX enhancements
• 🧪 Test coverage
• 🌐 Internationalization

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2025 Proteomorphic Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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🙏 Acknowledgments

Technologies & Tools

• ESM-2: Meta AI's protein language model
• 3Dmol.js: 3D molecular visualization library
• Flask: Python web framework
• Supabase: Backend-as-a-Service platform
• UniProt: Protein sequence database
• AlphaFold: Structure prediction (planned integration)

Research & Inspiration

• Protein Data Bank (PDB)
• National Center for Biotechnology Information (NCBI)
• Neurodegenerative disease research community
• Open-source bioinformatics community

Special Thanks

• All contributors and testers
• Academic advisors and mentors
• Open-source community

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📞 Contact & Support

Get in Touch

• GitHub Issues: Report bugs or request features
• Email: proteomorphic@example.com
• Documentation: Full documentation

Support the Project

If you find Proteomorphic useful, please consider:

• ⭐ Starring the repository
• 🐛 Reporting bugs
• 💡 Suggesting new features
• 📢 Sharing with colleagues

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📊 Project Status

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Made with ❤️ for the advancement of computational biology and neurodegenerative disease research

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