PepMEASURE: Methods for Extraction of Amino Acid Sequence Representations

PepMEASURE (Methods for Extraction of Amino Acid Sequence REpresentations) is available both as a Python package on PyPI and as a web server accessible through any modern web browser (https://pepmeasure.org). It enables comprehensive calculation of physicochemical properties for entire peptide datasets, either programmatically via Python or through an intuitive web interface. PepMEASURE provides detailed results for individual peptides, user-friendly visualizations, and supports comparative analyses across user-defined experimental or conditional groups. All computed features can be downloaded as tabular data for further analysis, and interactive visualizations can be exported as high-resolution static figures suitable for publication.

This work was conducted as part of the project 'Veni, Vidi, Visualization: Improving Analysis Communication for a Million-Dollar Machine' at the Data Analytics and Computational Statistics Chair, Hasso Plattner Institute.

📊 Web server

Guide

Visit https://pepmeasure.org to use PepMEASURE through an intuitive web interface.

Implementation

The web server's frontend was developed using Streamlit and the source code is available in a separate GitHub repository. All functionalities regarding the computation of features and visualisations are based on PepMEASURE.

💻 Python library

Installation

1. Make sure you have a Python package manager installed (e.g., pip or uv)
2. Install PepMEASURE
   pip install pepmeasure
3. Import and use PepMEASURE in your project!

Example

1. Initialize a calculator instance

   import pandas as pd
   from pepmeasure import Calculator
   calc = Calculator(
      dataset=pd.read_csv("path/to/dataset"),
      metadata=pd.read_csv("path/to/metadata"),
      seq="PEPTIDE",
   )
   

2. Select desired features and visualisations with individual parameters

   calc.set_feature_params(
      gravy=True,
      molecular_weight=True,
   )
   calc.set_plot_params(
      hydropathy_profile=True,
      classification=True,
      classification_classify_by="charge",
   )
   

3. Compute and show results

   print(calc.get_features())
   plots = calc.get_plots()
   for plot in plots:
      plot.show()
   

Optionally, custom colors can be used for the visualisations:

calc.set_colors(["#FF0000", "#00FF00", "#0000FF"])

Third-party resources

License

This project is licensed under the MIT License.