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README.md
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---
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license: mit
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task_categories:
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- feature-extraction
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language:
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- en
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tags:
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- biology
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- proteins
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- thermophiles
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- protein-engineering
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- alphafold
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size_categories:
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- n<1K
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---
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# APED: African Protein Engineering Dataset
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A curated dataset of protein structures from African thermophilic organisms, designed for machine learning applications in protein engineering.
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## Dataset Description
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This dataset contains structural and sequence features extracted from AlphaFold-predicted structures of proteins from thermophilic organisms found in African extreme environments (hot springs, volcanic regions).
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### Features (19 total)
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| Feature | Description |
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|---------|-------------|
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| `uniprot_id` | UniProt accession |
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| `organism` | Source organism |
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| `sequence` | Amino acid sequence |
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| `sequence_length` | Number of residues |
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| `mean_plddt` | Mean AlphaFold confidence |
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| `helix_fraction` | α-helix content |
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| `sheet_fraction` | β-sheet content |
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| `coil_fraction` | Coil content |
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| `hydrophobicity` | Mean hydrophobicity |
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| `charge` | Net charge at pH 7 |
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| `molecular_weight` | Molecular weight (Da) |
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| `isoelectric_point` | Predicted pI |
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| `instability_index` | Sequence instability |
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| `aromaticity` | Aromatic residue fraction |
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| And more... | |
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### Statistics
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- **500 proteins** with full features
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- **981 AlphaFold structures** downloaded
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- **3 novel designs** with pLDDT > 90%
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## Novel Protein Designs
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Included are 3 computationally designed proteins generated using:
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1. **RFdiffusion** - backbone generation
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2. **ProteinMPNN** - sequence design
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3. **AlphaFold** - structure validation
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Best design: **91.9% pLDDT, 0.74Å RMSD**
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## Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("electricsheepafrica/APED-African-Protein-Engineering-Dataset")
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```
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## Citation
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If you use this dataset, please cite:
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```
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@dataset{aped2024,
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title={APED: African Protein Engineering Dataset},
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author={Electric Sheep Africa},
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year={2024},
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publisher={HuggingFace}
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}
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```
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## License
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MIT License
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