---
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:**

**···**