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/\u003e\n\n[![PyPI](https://img.shields.io/pypi/v/idpet.svg)](https://pypi.org/project/idpet/)\n\u003c!-- [![DOI](https://img.shields.io/badge/DOI-10.5281/zenodo.1234567-blue)](https://doi.org/10.5281/zenodo.1234567) --\u003e\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CVyHXXSpO_sL-zf-7TCfKIzrY3_6lId1#scrollTo=bIKhG64_cwNX)\n---\n\n## IDPEnsembleTools: An Open-Source Library for Analysis of Conformational Ensembles of Disordered Proteins\n\nIDPEnsembleTools is a Python package designed to facilitate the **loading, analysis, and comparison** of multiple conformational ensembles of intrinsically disordered proteins (IDPs).\n\nIt supports various input formats such as `.pdb`, `.xtc`, and `.dcd`, and enables users to extract both **global** and **local** structural features, perform dimensionality reduction, and compute similarity scores between ensembles.\n\n\u003cimg src=\"https://raw.githubusercontent.com/BioComputingUP/EnsembleTools/main/images/pipline_example.jpeg\" alt=\"Pipeline Example\" width=\"600\" /\u003e\n\n---\n## Documentation\n\nFull documentation is available at:  \n[https://bioComputingUP.github.io/EnsembleTools](https://bioComputingUP.github.io/EnsembleTools)\n\n---\n## Features\n\nWith **IDPEnsembleTools**, you can:\n\n- **Extract global features** of structural ensembles:\n  - Radius of gyration (Rg)\n  - Asphericity\n  - Prolateness\n  - End-to-end distance\n\n- **Extract local features**:\n  - Interatomic distances\n  - Phi–psi angles\n  - Alpha-helix content\n\n- **Perform dimensionality reduction** on ensemble features:\n  - PCA\n  - UMAP\n  - t-SNE\n\n- **Compare structural ensembles** using:\n  - Jensen-Shannon (JS) divergence\n  - Visualize similarity matrices\n\n---\n\n## Example Notebooks\n\nThe `notebooks/` directory contains a collection of Jupyter notebooks that demonstrate how to use the `EnsembleTools` package. These examples cover key functionalities such as ensemble comparison, dimensionality reduction (PCA, t-SNE, UMAP), feature extraction, and visualization customization. They serve both as tutorials and reproducible workflows for analyzing disordered protein ensembles.\n\n| Notebook                      | Description                                                                                   | Link |\n|------------------------------|-----------------------------------------------------------------------------------------------|------|\n| `comparing_ensembles.ipynb`  | Compare multiple conformational ensembles using selected metrics and visualizations.          | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/comparing_ensembles.ipynb) |\n| `featurization.ipynb`        | Generate numerical features from protein ensembles for downstream analysis.                   | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/featurization.ipynb) |\n| `kpca_analysis.ipynb`        | Perform Kernel PCA to capture non-linear variance in ensemble structures.                     | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/kpca_analysis.ipynb) |\n| `loading_data.ipynb`         | Load and preprocess ensemble data from various formats.                                       | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/loading_data.ipynb) |\n| `pca_analysis.ipynb`         | Principal Component Analysis (PCA) for dimensionality reduction and visualization.            | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/pca_analysis.ipynb) |\n| `plot_customization.ipynb`   | Customize plots for clarity and publication-quality visualizations.                           | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/plot_customization.ipynb) |\n| `sh3_example.ipynb`          | Case study: global and local analysis of the SH3 domain of the Drk protein.                   | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/sh3_example.ipynb) |\n| `tsne_analysis.ipynb`        | t-SNE embedding of ensemble features to explore local structure.                              | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/tsne_analysis.ipynb) |\n| `umap_analysis.ipynb`        | UMAP embedding of ensemble features and visualization.                                | [View](https://github.com/BioComputingUP/EnsembleTools/blob/main/notebooks/umap_analysis.ipynb) |\n\n\n---\n## Installation\n\nIt is recommended to install `idpet` in a **clean virtual environment** to avoid conflicts with existing packages.\n\n\n### 🔹 Option 1: Using `conda` (if you use Anaconda/Miniconda)\n\n```bash\n# Create and activate a new conda environment\nconda create -n idpet-env python=3.9\nconda activate idpet-env\n\n# Install the package from PyPI\npip install idpet\n```\n\n\n### 🔹 Option 2: Using `venv` (standard Python)\n\n```bash\n# Create a new virtual environment (Python 3.7+)\npython -m venv idpet-env\n\n# Activate the environment\n# On Linux/macOS:\nsource idpet-env/bin/activate\n# On Windows:\nidpet-env\\Scripts\\activate\n\n# Upgrade pip and install the package\npip install --upgrade pip\npip install idpet \n```\n\n### Developer Installation (from source)\n```bash\ngit clone https://github.com/BioComputingUP/EnsembleTools.git\ncd idpet\npip install -e .\n```\n\n## License\nThis project is licensed under the [MIT License](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocomputingup%2Fensembletools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiocomputingup%2Fensembletools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocomputingup%2Fensembletools/lists"}