IApred: Intrinsic Antigenicity Predictor 🧬

IApred is a powerful tool for predicting the intrinsic antigenicity of pathogen proteins in a host-independent manner. Our predictor leverages a manually curated dataset spanning multiple pathogen types and host species to provide accurate antigenicity predictions.

Try IApred instantly in your browser:

# Clone the repository
git clone https://github.com/sebamiles/IApred.git
cd IApred

# Install dependencies
pip install -r requirements.txt

• Python 3.6+
• Dependencies:
  • numpy
  • biopython
  • scikit-learn
  • joblib
  • scipy

python IApred.py input_fasta_file [output_csv_file]

python IApred.py test.fasta
# or
python IApred.py test.fasta test_results.csv

Processing sequence: >Protein_X
Intrinsic Antigenicity: 0.63 (High)
Low                        Moderate                        High
[---------------------------|---0---|---------------------------]
                                       ^
                                     0.63

Processing FASTA file: test.fasta
Found 120 sequences
Processing: 120/120

Antigenicity Summary:
Low Antigenicity (score < -0.3): 18 sequences
Moderate Antigenicity (-0.3 to 0.3): 29 sequences
High Antigenicity (score > 0.3): 73 sequences

• Score Range: Typically -3 to 3
• Categories:
  • High: > 0.3
  • Moderate: -0.3 to 0.3
  • Low: < -0.3

Note: The predictor focuses on amino acid sequence-based antigenicity. Actual antigenicity may be influenced by additional factors such as structure, post-translational modifications and epitope availability.

1. Verify installation of required packages
2. Ensure presence of:
   • models folder with all .joblib files
   • functions.py
   • protein_motifs.txt
3. Check FASTA file formatting

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

If you use IApred in your research, please cite:

[Citation information will be added upon publication]

Contributions are welcome! Please feel free to submit a Pull Request.

For support or queries, please open an issue or contact [smiles@higiene.edu.uy].