README.md

metadata

title: Protein Structure Predictor
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

Protein Structure Predictor - CPU-based Analysis

AI-powered protein structure prediction using established bioinformatics methods and machine learning, optimized for CPU execution.

🧬 Features

• 🔬 Secondary Structure Prediction: Predict helix, sheet, and coil regions using ML
• ⚔️ Protease Site Analysis: Identify potential cleavage sites for common proteases
• 📊 Protein Properties: Calculate molecular weight, pI, instability index, and more
• 🧪 Interactive Interface: User-friendly web interface for researchers
• 📚 PDB Generation: Create structure files for visualization
• 🖥️ CPU Optimized: Fast execution without GPU requirements

🏗️ Technology Stack

┌─────────────────────────────────────────┐
│        Protein Structure Predictor     │
├─────────────────────────────────────────┤
│  Gradio Frontend (Port 7860)           │
├─────────────────────────────────────────┤
│  BioPython + scikit-learn ML            │
├─────────────────────────────────────────┤
│  CPU-based Prediction Pipeline         │
├─────────────────────────────────────────┤
│  Python 3.10 + Scientific Libraries    │
└─────────────────────────────────────────┘

📦 Method Information

Prediction Approach

• Type: Machine learning-based structure prediction
• Libraries: BioPython, scikit-learn, NumPy, Pandas
• Input: Amino acid sequences (10-2000 residues)
• Output: Secondary structure, protease sites, PDB files, protein properties
• Performance: Fast CPU execution, ~1-5 seconds per sequence

Supported Features

• Secondary structure prediction (α-helix, β-sheet, coil)
• Protease cleavage site prediction (Trypsin, Chymotrypsin, Pepsin)
• Protein property analysis (MW, pI, instability, hydrophobicity)
• Simple PDB structure generation
• Confidence scoring for predictions

🚀 Usage

Input Requirements

• Format: Single-letter amino acid codes (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y)
• Length: 10-2000 amino acids
• Examples:
  • Short peptide: MKFLVNVALVFMVVYISYIYA
  • Protein domain: MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWUQTPACVTYFTQSSLASRQGFVDWDDAASRPAINVGLYPTLNTVGGHQAAMQMLKETINEEAAEWDRVHPVHAGPIAPGQMREPRGTHGTWTIMHPSPSTEEGHAIPQRQTPSPGDGPVVPSASLYAVSPAILPKDGPVVVSQVKQWRQEFGWVLTPWVQTIIDGRGEEQTFLPGQHFLRELQJKHNLNHEFRLQTLLLTCDENGKGPLPQIVIRGQGDSREQAPGQWLEQPGWASPATCSPGPPRPPRPPPPPPPPPPPPPPP

Workflow

1. Load Models: Click "🚀 Load Prediction Models" to initialize the system
2. Input Sequence: Enter or paste your protein sequence
3. Predict Structure: Click "🔬 Predict Structure" to run analysis
4. Review Results: Examine predictions, properties, and PDB structure
5. Export Data: Download PDB files for further analysis

📊 Output Information

Secondary Structure Prediction

• Helix (H): α-helical regions with confidence scores
• Sheet (E): β-sheet regions with structural context
• Coil (C): Random coil and loop regions

Protein Properties

• Molecular Weight: Calculated from amino acid composition
• Isoelectric Point: pH at which protein has no net charge
• Instability Index: Measure of protein stability in solution
• GRAVY Score: Grand average of hydropathy (hydrophobicity)
• Aromaticity: Fraction of aromatic amino acids

Protease Analysis

• Cleavage Sites: Predicted positions where proteases may cut
• Site Context: Amino acids surrounding cleavage sites
• Protease Types: Trypsin, Chymotrypsin, Pepsin predictions

🔧 Technical Details

Machine Learning Approach

• Algorithm: Random Forest classifier for secondary structure
• Features: Amino acid properties in sliding windows
• Training: Synthetic data for demonstration (real implementation would use PDB data)
• Validation: Cross-validation and confidence scoring

Computational Requirements

• Memory: ~100-500 MB RAM for typical sequences
• Processing Time: 1-5 seconds depending on sequence length
• CPU Usage: Single-threaded, optimized for HF Spaces

🧪 Research Applications

Structural Biology

• Protein Characterization: Analyze unknown protein sequences
• Domain Analysis: Identify structural domains and motifs
• Comparative Studies: Compare structures across species

Drug Discovery

• Target Analysis: Understand protein structure for drug design
• Binding Site Prediction: Identify potential drug binding regions
• Stability Assessment: Evaluate protein stability for therapeutics

Biotechnology

• Protein Engineering: Design proteins with desired properties
• Enzyme Analysis: Study enzyme structure-function relationships
• Biomarker Discovery: Identify structural features for diagnostics

📚 Example Use Cases

Case 1: Enzyme Analysis

Input: Protease enzyme sequence
Output: Active site prediction, substrate specificity
Application: Industrial enzyme optimization

Case 2: Therapeutic Protein

Input: Antibody or hormone sequence
Output: Stability analysis, potential degradation sites
Application: Biopharmaceutical development

Case 3: Membrane Protein

Input: Transmembrane protein sequence
Output: Secondary structure, hydrophobic regions
Application: Drug target analysis

🔗 Related Resources

• 🧬 BioPython Documentation: https://biopython.org/
• 📊 scikit-learn: https://scikit-learn.org/
• 📚 Protein Structure Databases: PDB, UniProt, SCOP
• 🔬 Protease Databases: MEROPS, CutDB

🤝 Contributing

We welcome contributions to improve the protein structure predictor:

• Algorithm Improvements: Enhance prediction accuracy
• Feature Additions: Add new analysis capabilities
• Performance Optimization: Improve speed and efficiency
• Documentation: Help improve user guides and examples

📄 Citation

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

@misc{protein-predictor-2024,
  title={CPU-based Protein Structure Predictor},
  author={gsstec},
  year={2024},
  url={https://huggingface.co/spaces/gsstec/protein-predictor}
}

📞 Support

For questions, issues, or collaboration opportunities:

• GitHub Issues: Report bugs and request features
• HuggingFace Discussions: Community support and discussions
• Email: Contact for research collaborations

---

Disclaimer: This tool is for research purposes. Predictions should be validated experimentally for critical applications. The current implementation uses simplified models for demonstration - production use would require training on actual structural databases.