Datasets:
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---
dataset_info:
features:
- name: seq
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 24941535
num_examples: 10848
- name: test
num_bytes: 1665908
num_examples: 667
download_size: 3931715
dataset_size: 26607443
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- token-classification
tags:
- chemistry
- biology
size_categories:
- 10K<n<100K
---
# Dataset Card for Secondary Structure Prediction (Q8) Dataset
### Dataset Summary
The study of a protein’s secondary structure (Sec. Struc. P.) forms a fundamental cornerstone in understanding its biological function. This secondary structure, comprising helices, strands, and various turns, bestows the protein with a specific three-dimensional configuration, which is critical for the formation of its tertiary structure. In the context of this work, a given protein sequence is classified into three distinct categories, each representing a different structural element: H - Alpha-helix, G - 3-10 helix, I - Pi helix, E - Beta-strand, B - Beta-bridge, T - Turn, S - Bend, C - Coil (or random coil).
## Dataset Structure
### Data Instances
For each instance, there is a string of the protein sequences, a sequence for the strucutral labels. See the [Secondary structure prediction dataset viewer](https://huggingface.co/datasets/Bo1015/ssp_q8/viewer/default/test) to explore more examples.
```
{'seq':'GKITFYEDRGFQGRHYECSSDHSNLQPYFSRCNSIRVDSGCWMLYEQPNFQGPQYFLRRGDYPDYQQWMGLNDSIRSCRLIPHTGSHRLRIYEREDYRGQMVEITEDCSSLHDRFHFSEIHSFNVLEGWWVLYEMTNYRGRQYLLRPGDYRRYHDWGATNARVGSLRRAVDFY'
'label':[ 7, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 7, 6, 6, 6, 7, 5, 7, 7, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 7, 5, 5, 5, 7, 7, 7, 4, 4, 4, 4, 4, 7, 7, 7, 5, 7, 7, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 5, 6, 6, 6, 5, 5, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 5, 7, 4, 4, 4, 4, 5, 5, 5, 5, 5, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 7, 5, 0, 0, 0, 0, 6, 7, 5, 5, 7, 7, 7, 7, 4, 4, 4, 4, 7, 7, 7, 7, 7 ]}
```
The average for the `seq` and the `label` are provided below:
| Feature | Mean Count |
| ---------- | ---------------- |
| seq | 256 |
| label (0) | 9 |
| label (1) | 82 |
| label (2) | 1 |
| label (3) | 3 |
| label (4) | 52 |
| label (5) | 26 |
| label (6) | 19 |
| label (7) | 63 |
### Data Fields
- `seq`: a string containing the protein sequence
- `label`: a sequence containing the structural label of each residue.
### Data Splits
The secondary structure prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 10,848 |
| Test | 667 |
### Source Data
#### Initial Data Collection and Normalization
The datasets applied in this study were originally published by [NetSurfP-2.0](https://pubmed.ncbi.nlm.nih.gov/30785653/).
### Licensing Information
The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation
If you find our work useful, please consider citing the following paper:
```
@misc{chen2024xtrimopglm,
title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
year={2024},
eprint={2401.06199},
archivePrefix={arXiv},
primaryClass={cs.CL},
note={arXiv preprint arXiv:2401.06199}
}
``` |