alexchilton/gran-nanobody-proteins

GRAN Protein Structure Dataset

Dataset Description

This dataset contains protein graph data for training Graph Recurrent Attention Networks (GRAN) for protein sequence and structure generation.

Dataset Summary

• Number of proteins: 2965
• Average protein length: 121.0 residues
• Unique amino acids: 22
• Source: Nanobody protein structures
• Created by: alexchilton
• Date: 2025-05-04 16:01:38

Dataset Structure

Each protein entry contains:

• sequence: Complete amino acid sequence
• sequence_length: Total length of the protein
• graph_nodes: List of graph nodes (residue indices)
• graph_edges: List of graph edges (connections between residues)
• adjacency_matrix: Binary adjacency matrix representing contacts
• node_features: Features for each node (Meiler features or one-hot encoded residues)
• protein_id: Unique identifier

Amino Acids

Available amino acids: ALA, ARG, ASN, ASP, CYS, GLN, GLU, GLY, HIS, ILE, LEU, LYS, MET, PHE, PRO, SER, THR, TRP, TYR, UNK, VAL, X

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alexchilton/gran-nanobody-proteins")

# Access a protein
protein = dataset['train'][0]
print(f"Sequence length: {protein['sequence_length']}")
print(f"Number of graph nodes: {len(protein['graph_nodes'])}")
print(f"Adjacency matrix shape: {np.array(protein['adjacency_matrix']).shape}")

Training GRAN Model

This dataset is designed for training GRAN models that:

1. Generate both protein sequences and contact adjacency matrices
2. Model proteins as graphs with nodes (residues) and edges (contacts)
3. Use node features (Meiler descriptors or one-hot encoding)

Citation

If you use this dataset, please cite:

@dataset{gran_protein_structures,
  title={GRAN Protein Structure Dataset},
  author={Alex Chilton},
  year={2025},
  url={https://huggingface.co/datasets/alexchilton/gran-nanobody-proteins}
}