README.md

metadata

license: mit
tags:
  - rna
  - biology
  - rna-design
  - biomolecule-design
  - 3d-design
  - inverse-folding
  - inverse-design

gRNAde Datasets

This repository contains datasets and wet-lab experimental data for gRNAde, a generative AI framework for RNA inverse design.

📦 Datasets and Experimental Data

.
├── RNASolo_31102023_processed.pt       # Pre-processed training dataset (ready for ML)
├── RNASolo_31102023_raw.tar.gz         # Raw PDB structures from RNASolo
└── projects                            # Data for reproducing design campaigns in the paper
    ├── openknot_benchmark              # Eterna OpenKnot Benchmark for psuedoknotted RNA design
    └── rna_polymerase_ribozyme         # Generative Design of RNA polymerase ribozymes

Pre-processed Training Dataset

• File: RNASolo_31102023_processed.pt
• Description: ML-ready dataset of RNA 3D structures from the Protein Data Bank
• Source: RNASolo database (October 31, 2023 cutoff)
• Resolution: ≤4.0 Å
• Format: PyTorch tensors with sequences, 3D coordinates, secondary structures, and metadata
• Use case: Training new gRNAde models or fine-tuning for specific RNA families

Raw PDB Structures

• File: RNASolo_31102023_raw.tar.gz
• Description: Raw PDB files for all RNA structures from RNASolo
• Contents: Thousands of experimentally determined RNA structures
• Use case: Custom data processing pipelines or structure visualization

Eterna OpenKnot Competition Data

• Directory: projects/openknot_benchmark/
• Description: Designs and experimental validation data from the Eterna OpenKnot Benchmark
• Contents: Chemical probing data, OpenKnot scores, and designed sequences
• Targets: 40 pseudoknotted RNA puzzles including riboswitches, ribozymes, and viral elements

RNA Polymerase Ribozyme Data

• Directory: projects/rna_polymerase_ribozyme/
• Description: Functional design campaign data for engineering RNA polymerase ribozymes
• Contents: Generated sequences, activity assays, fitness landscapes

🚀 Quick Start

After setting up the gRNAde codebase, datasets can be downloaded manually or using HuggingFace CLI (recommended):

# Ensure you are in the base directory
cd ~/geometric-rna-design

# Install HuggingFace CLI (https://huggingface.co/docs/huggingface_hub/main/en/guides/cli)
pip install -U "huggingface_hub[cli]"
# alternate: curl -LsSf https://hf.co/cli/install.sh | bash
# alternate: brew install huggingface-cli

# Download all datasets to data/ directory
huggingface-cli download chaitjo/gRNAde_datasets --local-dir data/ --repo-type dataset

Alternately, download specific files:

# Download only the pre-processed training dataset
huggingface-cli download chaitjo/gRNAde_datasets RNASolo_31102023_processed.pt --local-dir data/ --repo-type dataset

# Download only raw PDB structures
huggingface-cli download chaitjo/gRNAde_datasets RNASolo_31102023_raw.tar.gz --local-dir data/ --repo-type dataset

Citations

@article{joshi2025generative,
  title={Generative inverse design of RNA structure and function with g{RNA}de},
  author={Joshi, Chaitanya K and Gianni, Edoardo and Kwok, Samantha LY and Mathis, Simon V and Lio, Pietro and Holliger, Philipp},
  journal={bioRxiv},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

@inproceedings{joshi2025grnade,
  title={g{RNA}de: Geometric Deep Learning for 3D RNA inverse design},
  author={Joshi, Chaitanya K and Jamasb, Arian R and Vi{\~n}as, Ramon and Harris, Charles and Mathis, Simon V and Morehead, Alex and Anand, Rishabh and Li{\`o}, Pietro},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2025},
}

@incollection{joshi2024grnade,
  title={g{RNA}de: A Geometric Deep Learning pipeline for 3D RNA inverse design},
  author={Joshi, Chaitanya K and Li{\`o}, Pietro},
  booktitle={RNA Design: Methods and Protocols},
  pages={121--135},
  year={2024},
  publisher={Springer}
}