| ---
|
| license: mit
|
| ---
|
| # Description
|
| Binary Localization prediction is a binary classification task where each input protein *x* is mapped to a label *y* ∈ {0, 1}, corresponding to either "membrane-bound" or "soluble" .
|
|
|
| The digital label means:
|
|
|
| 0: membrane-bound
|
|
|
| 1: soluble
|
|
|
| # Splits
|
|
|
| **Structure type:** AF2
|
|
|
| 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:
|
|
|
| - Train: 6707
|
| - Valid: 698
|
| - Test: 807
|
|
|
| # Data format
|
|
|
| We organize all data in LMDB format. The architecture of the databse is like:
|
|
|
| **length:** The number of samples
|
|
|
| **0:**
|
|
|
| - **name:** The UniProt ID of the protein
|
|
|
| - **seq:** The structure-aware sequence
|
| - **plddt**: pLDDT values at all positions
|
| - **label:** classification label of the sequence
|
|
|
| **1:**
|
|
|
| **···** |
