Demo

We designed the IDPET Python package to facilitate the study and analysis of conformational ensembles of intrinsically disordered proteins. These proteins, known for their dynamic and highly variable structures, play a fundamental role in various biological processes.

Here, we highlight four different types of analyses that can be performed using IDPET. For more details you can also check the jupyter notebooks provided in the github repository.

Notebooks Overview

Here’s a summary of the example notebooks available in the github repository:

comparing_ensembles

Compare multiple conformational ensembles using selected metrics and visualizations.

featurization

Generate numerical features from protein ensembles for downstream analysis.

kpca_analysis

Perform Kernel PCA to capture non-linear variance in ensemble structures.

loading_data

Load and preprocess ensemble data from various formats.

pca_analysis

Principal Component Analysis (PCA) for dimensionality reduction and visualization.

plot_customization

Customize plots for clarity and publication-quality visualizations.

sh3_example

Case study: global and local analysis of the SH3 domain of the Drkn protein.

tsne_analysis

t-SNE embedding of ensemble features to explore local structure.

umap_analysis

UMAP embedding for global manifold learning and visualization.

idpet_vs_soursop

A comparative analysis between IDPET and Soursop packages for ensemble analysis.

MDP

Example to perform analysis for Multi Domain Proteins.

alpha_synuclein

Analyze the structural features and dynamics of 5 different ensembles of the alpha-synuclein protein.

Global analysis

Local analysis

Dimensionality Reduction analysis

Ensemble Comparison