antifungal 0.1.2

A python package for activity prediction and design of antifungal peitdes.

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, follow these steps:

# Create a virtual environment (optional but recommended)
pip install virtualenv
virtualenv env --python=python3.8
env\Scripts\activate # For Windows
source env/bin/activate # For Linux

# install antifungal package with pip
pip install antifungal

Usage

The Antifungal Peptide Prediction Tool serves multiple purposes including the prediction of antifungal activities and the rational design of peptides. It enables users to segment peptide sequences, perform single-point mutation analysis, and globally optimize peptide sequences for enhanced properties.

Example Usage for antifungal activity 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("HIHIRHMWLLRRR")
segment_predictions = segment_instance.get_segmented_sequences().predict()
print(segment_predictions)
# Expected output: 
{
    'antifungal': [True, True, True, True, True, True],
    'prob_antifungal': [95.2, 97.9, 98.2, 97.7, 97.5, 99.0],
    'MIC_C_albicans': [21.8, 17.34, 21.03, 19.68, 25.91, 24.36],
    'prob_MIC_C_albicans': [99.8, 99.8, 99.8, 99.8, 99.8, 99.8],
    'MIC_C_krusei': [7.13, 5.87, 5.85, 6.45, 5.96, 4.7],
    'prob_MIC_C_krusei': [99.3, 99.4, 99.7, 99.0, 99.5, 98.9],
    'MIC_C_neoformans': [24.4, 15.57, 10.01, 16.36, 10.46, 16.2],
    'prob_MIC_C_neoformans': [99.3, 99.6, 99.8, 99.6, 99.9, 99.5],
    'MIC_C_parapsilosis': [18.3, 17.05, 17.08, 18.28, 18.21, 19.01],
    'prob_MIC_C_parapsilosis': [84.5, 82.6, 85.6, 83.7, 82.4, 85.2],
    'AFI': [16.23, 12.82, 12.04, 13.96, 13.1, 13.7],
    'prob_AFI': [79.16, 79.9, 83.47, 80.47, 79.7, 82.84],
    'peptide_seq': ['HIHIRHMWLLR', 'HIHIRHMWLLRR', 'HIHIRHMWLLRRR', 'IHIRHMWLLRR', 'IHIRHMWLLRRR', 'HIRHMWLLRRR'],
    'seq_name': ['segment_1_11', 'segment_1_12', 'segment_1_13', 'segment_2_12', 'segment_2_13', 'segment_3_13']
}

# Example for single_point_mutation class
mutation_instance = single_point_mutation("HIHIRHMWLLRRR")
mutation_predictions = mutation_instance.get_mutated_sequences().predict()
print(mutation_predictions)
# Expected output for single_point_mutation:
{
    "antifungal": [true, true, ...], 
    "prob_antifungal": [100.0, 95.8, ...], 
    "MIC_C_albicans": [28.24, 23.48, ...], 
    "prob_MIC_C_albicans": [99.9, 99.8, ...], 
    "MIC_C_krusei": [8.37, 8.57, ...], 
    "prob_MIC_C_krusei": [99.9, 99.9, ...], 
    "MIC_C_neoformans": [6.58, 5.2, ...], 
    "prob_MIC_C_neoformans": [99.7, 99.2, ...], 
    "MIC_C_parapsilosis": [27.36, 23.58, ...], 
    "prob_MIC_C_parapsilosis": [86.9, 87.2, ...], 
    "AFI": [14.37, 12.54, ...],
    "prob_AFI": [86.47, 82.62, ...], 
    "peptide_seq": ["AIHIRHMWLLRRR", "CIHIRHMWLLRRR", ...], 
    "seq_name": ["mutate_1_A", "mutate_1_C", ...] 
}


# Example for global_optimization class, this will take a few minutes to hours to run depending on the number of iterations and sequence length
optimization_instance = global_optimization("HIHIRHMWLLRRR")
optimized_seq, results = optimization_instance.optimize()
print(results)
# Expected output for global_optimization:
{
    "optimized_seq": "FICFRCMWFCRRL",
    "antifungal_idx": [3.96]
}

Directory Structure

• data/: Contains data used for model development.

  • training_data/: Stores the datasets utilized in training the predictive models.
  • screening_data: Contains data from extensive screening studies detailed in the referenced article.

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

• propy/: A modified version of the propy package, optimized for enhanced performance and bug fixes. The original package can be found at propy.

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

Reference

For a comprehensive understanding and methodological details, refer to the paper "Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship," published in ACS Med. Chem. Lett. (2022, 13, 1, 99–104)[link]](https://pubs.acs.org/doi/10.1021/acsmedchemlett.1c00556). For additional resources and online tools, visit the Antifungal Webserver.

License

This project is licensed under the MIT License, which allows for broad usage and modification under specific terms.