antifungal 0.1.0

A python package for antifungal peptide prediction.

Antifungal Peptide Prediction Tool

This repository hosts the Antifungal Peptide Prediction Tool, a Python package for predicting and analyzing antifungal peptides. It integrates various functionalities including peptide sequence processing, descriptor calculation, and machine learning-based prediction models.

Installation

To install the package, run the following command:

# optional: create a virtual environment
pip install virtualenv
virtualenv env --python=python3.8
env\Scripts\activate # activate the virtual environment on Windows
source env/bin/activate # activate the virtual environment on Linux

# install antifungal package with pip
pip install antifungal

Usage

The tool can be used to predict antifungal activities of peptides, as well as rational design, including segment peptide sequences, perform single-point mutation analysis, and globally optimize peptide sequences for enhanced properties.

Example Usage for antifungal peptide prediction

from antifungal.predict import predict_MIC

seq = ['HIHIRHMWLLR','HIHIRHMWLLRR']
pred = predict_MIC(seq)
print(pred)
# Expected output: 
# {
    # 'antifungal': [True, True],
    # 'prob_antifungal': [95.2, 97.9],
    # 'MIC_C_albicans': [21.8, 17.34],
    # 'prob_MIC_C_albicans': [99.8, 99.8],
    # 'MIC_C_krusei': [7.13, 5.87],
    # 'prob_MIC_C_krusei': [99.3, 99.4],
    # 'MIC_C_neoformans': [24.4, 15.57],
    # 'prob_MIC_C_neoformans': [99.3, 99.6],
    # 'MIC_C_parapsilosis': [18.3, 17.05],
    # 'prob_MIC_C_parapsilosis': [84.5, 82.6],
    # 'AFI': [16.23, 12.82],
    # 'prob_AFI': [79.16, 79.9],
    # 'peptide_seq': ['HIHIRHMWLLR', 'HIHIRHMWLLRR']}
# }

Example Usage for antifungal peptide design

from antifungal.design import segment, single_point_mutation, global_optimization

# Example for segment class
segment_instance = segment("YOUR_PEPTIDE_SEQUENCE")
segment_predictions = segment_instance.get_segmented_sequences().predict()

# Example for single_point_mutation class
mutation_instance = single_point_mutation("YOUR_PEPTIDE_SEQUENCE")
mutation_predictions = mutation_instance.get_mutated_sequences().predict()

# Example for global_optimization class
optimization_instance = global_optimization("YOUR_PEPTIDE_SEQUENCE")
optimized_seq, results = optimization_instance.optimize()

Directory Structure

• data/: Contains training data used for model development.

  • training_data/: Stores the datasets utilized in training the predictive models.
  • screening_data: Stores the screening results in the following article.

• model/: Houses the trained models for antifungal peptide prediction.

• propy/: Includes a modified version of the propy package, with bug fixes. The original package can be found at PyPI propy.

• ChemoinfoPy/: Contains Python scripts for variable selection, peptide sequence preprocessing, descriptor calculation, and dataset partitioning for correction and validation purposes.

Reference

For more detailed information, refer to the paper Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship, ACS Med. Chem. Lett. 2022, 13, 1, 99–104 and visit the Antifungal Webserver.

License

MIT