🧬 DiffEnsemble: Differentiable IDP Ensemble Prediction

DiffEnsemble is a JAX-powered framework for predicting structural ensembles of Intrinsically Disordered Proteins (IDPs) using a Variational Autoencoder (VAE) coupled with differentiable biophysical observables.

---

🧪 For Structural Biologists

• Ensemble Averaging: Automatically calculates ensemble-averaged SAXS profiles and NMR observables.
• Disorder Recovery: Specifically designed for proteins that don't have a single "fixed" structure, providing a statistical view of the conformational landscape.

🤖 For Machine Learning Geeks

• VAE-Physics Integration: A latent-space generative model where the reconstruction loss is a combination of latent KLD and physical observables (SAXS/NMR).
• Differentiable Torsions: Maps latent vectors to 3D coordinates via a differentiable NeRF (Natural Extension Reference Frame) implementation.

---

🚀 Key Features

• JAX-Accelerated VAE: High-performance training of generative models for IDPs.
• Debye-Based SAXS Prediction: Differentiable back-calculation of SAXS profiles from structural ensembles.
• Latent Space Exploration: Sample new conformations from the learned disordered landscape.

📦 Installation

pip install diff-ensemble

📖 Tutorials

Get started immediately with our interactive Jupyter notebooks:

• Quick Start: Differentiable IDP Ensemble Prediction: Train a VAE to predict structural ensembles constrained by SAXS data.

📜 License

Distributed under the MIT License. See LICENSE for more information.