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| license: mit
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| ---
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| # Description
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| Subcellular Localization prediction is a 10-class classification task to predict where a protein locates in the cell, where each input protein *x* is mapped to a label *y* ∈ {0, 1, ..., 9}.
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| The digital label means:
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| 0: Nucleus
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| 1: Cytoplasm
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| 2: Extracellular
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| 3: Mitochondrion
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| 4: Cell.membrane
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| 5: Endoplasmic.reticulum
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| 6: Plastid
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| 7: Golgi.apparatus
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| 8: Lysosome/Vacuole
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| 9: Peroxisome
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| # Splits
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| **Structure type:** AF2
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| The dataset is from [**DeepLoc: prediction of protein subcellular localization using deep learning**](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857). We employ all proteins (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below:
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| - Train: 10414
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| - Valid: 1368
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| - Test: 1368
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| # Data format
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| We organize all data in LMDB format. The architecture of the databse is like:
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| **length:** The number of samples
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| **0:**
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| - **name:** The UniProt ID of the protein
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| - **seq:** The structure-aware sequence
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| - **plddt**: pLDDT values at all positions
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| - **label:** classification label of the sequence
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| **1:**
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| **···** |
