Protein Mutation Analyzer

A Python-based bioinformatics tool for analyzing the effects of point mutations on protein properties.

Overview

This tool predicts how single amino acid substitutions (point mutations) affect key protein characteristics:

• Molecular weight - Mass changes in Daltons
• Electric charge - Changes in net charge at pH 7
• Hydrophobicity - Changes in water affinity (Kyte-Doolittle scale)

Mutations are automatically classified as:

• 🟢 Conservative - Minor effects on protein properties
• 🟡 Moderate - Intermediate effects
• 🔴 Radical - Major effects potentially disrupting protein structure/function

Features

✅ Load protein sequences in FASTA format
✅ Analyze multiple mutations from CSV files
✅ Calculate property changes with validation
✅ Classify mutations by severity
✅ Generate publication-quality visualizations
✅ Export professional HTML reports
✅ Built-in error checking and validation

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup

# Clone the repository
git clone https://github.com/SimPet01/protein-mutation-analyzer.git
cd protein-mutation-analyzer

# Install dependencies
pip install -r requirements.txt

Quick Start

1. Prepare your input files

Protein sequence (data/my_protein.fasta):

>sp|P02144|MYO_HUMAN Myoglobin
MGLSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASED

Mutations (data/my_mutations.csv):

position,original_aa,mutant_aa,description
7,E,K,Glutamic acid to Lysine - charge reversal
22,I,V,Isoleucine to Valine - conservative

2. Run the analysis

cd scripts
python analyzer.py

3. View results

The tool generates three output files in the output/ directory:

• mutation_analysis.csv - Detailed results table
• mutations_visualization.png - Four-panel visualization
• mutation_report.html - Interactive HTML report

Project Structure

protein-mutation-analyzer/
├── README.md                    # Project overview (you are here)
├── USAGE.md                     # Detailed usage guide
├── requirements.txt             # Python dependencies
├── data/                        # Input files
│   ├── example_protein.fasta
│   ├── mutations_example.csv
├── scripts/                     # Python code
│   ├── analyzer.py              # Main analyzer class
│   └── check_positions.py       # Testing script for aminoacids position
└── output/                      # Generated results
    ├── mutation_analysis.csv
    ├── mutations_visualization.png
    └── mutation_report.html

Usage Examples

Basic Usage

from analyzer import ProteinMutationAnalyzer

# Initialize with protein sequence
analyzer = ProteinMutationAnalyzer('../data/example_protein.fasta')

# Load mutations
analyzer.load_mutations('../data/mutations_example.csv')

# Perform analysis
results = analyzer.analyze_all_mutations()

# Generate outputs
analyzer.save_results()
analyzer.visualize_mutations()
analyzer.generate_html_report()

Programmatic Access

# Access results DataFrame
results_df = analyzer.results_df

# Filter specific mutations
radical_mutations = results_df[results_df['classification'] == 'Radical']

# Get specific properties
charge_changes = results_df['charge_change']

Mutation Classification Logic

The tool uses the following criteria to classify mutations:

Classification	Criteria
Conservative	MW change < 5 Da AND no charge change AND hydrophobicity change < 0.5
Radical	MW change > 20 Da OR charge change ≠ 0 OR hydrophobicity change > 1.5
Moderate	Everything in between

Scientific Background

Amino Acid Properties

The analyzer uses established biochemical scales:

• Hydrophobicity: Kyte-Doolittle scale

  • Positive values = hydrophobic (water-avoiding)
  • Negative values = hydrophilic (water-attracting)

• Charge: At physiological pH (7,0)

  • Basic residues (K, R, H): +1
  • Acidic residues (D, E): -1
  • Others: 0 (neutral)

• Molecular Weight: Standard amino acid masses (Da)

Amino Acid Grouping

Group	Residues	Properties
Hydrophobic	A, V, I, L, M, F, W, P	Nonpolar, often buried in protein core
Polar	S, T, C, Y, N, Q	Uncharged but interact with water
Charged+	K, R, H	Positively charged (basic)
Charged-	D, E	Negatively charged (acidic)
Special	G	Smallest, most flexible

Example Output

Visualization

Four-panel analysis showing molecular weight, charge, and hydrophobicity changes, plus classification distribution.

HTML Report

The HTML report provides an interactive, color-coded table viewable in any web browser.

Dependencies

• biopython (1.81) - Sequence parsing and protein analysis
• pandas (2.0.0) - Data manipulation and CSV handling
• matplotlib (3.7.0) - Visualization generation
• numpy (1.24.0) - Numerical operations

See requirements.txt for complete list.

Documentation

• README.md - This file, project overview
• USAGE.md - Comprehensive usage guide with examples
• Code documentation - Inline docstrings following Python standards

Limitations

• Currently supports only single point mutations (not insertions/deletions)
• Predictions are based on physicochemical properties, not structural modeling
• Does not account for protein context (local structure, interactions)
• Charge calculations use simplified pH 7,0 model

Future Enhancements

Potential improvements for future versions:

• Integration with AlphaFold for structural context
• Support for multiple mutation combinations
• Machine learning-based severity prediction
• Batch processing of multiple proteins
• Web interface for non-programmers
• Integration with protein databases (UniProt, PDB)

Contributing

This is a personal learning project created for bioinformatics portfolio development. Suggestions and feedback are welcome!

License

This project is open source and available for educational and research purposes.

Author

Created as part of bioinformatics skill development and job application portfolio.

Contact: karelDOTcurinaATseznamDOTcz

Acknowledgments

• BioPython community for excellent biological sequence tools
• Kyte & Doolittle for the hydrophobicity scale
• Swiss-Prot/UniProt for protein sequence data standards

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Note: This tool is for research and educational purposes. For clinical or production use, additional validation and testing would be required.