Update README.md

README.md

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----
-license: mit
----
-# Description
-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}.
-
-The digital label means:
-
-0: Nucleus
-
-1: Cytoplasm
-
-2: Extracellular
-
-3: Mitochondrion
-
-4: Cell.membrane
-
-5: Endoplasmic.reticulum
-
-6: Plastid
-
-7: Golgi.apparatus
-
-8: Lysosome/Vacuole
-
-9: Peroxisome
-
-# 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: 10414
-- Valid: 1368
-- Test:  1368
-
-# 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:**
-
+---
+license: mit
+---
+# Description
+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}.
+
+The digital label means:
+
+0: Nucleus
+
+1: Cytoplasm
+
+2: Extracellular
+
+3: Mitochondrion
+
+4: Cell.membrane
+
+5: Endoplasmic.reticulum
+
+6: Plastid
+
+7: Golgi.apparatus
+
+8: Lysosome/Vacuole
+
+9: Peroxisome
+
+# 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: 10414
+- Valid: 1368
+- Test:  1368
+
+# 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
+- **label:** classification label of the sequence
+
+**1:**
+
 **···**