---
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: 3610640
  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 (Q3) 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 - Helix (includes alpha-helix, 3-10 helix, and pi helix), E - Strand (includes beta-strand and beta-bridge), C - Coil (includes turns, bends, and random coils).

## 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':'MRGSHHHHHHGSVKVKFVSSGEEKEVDTSKIKKVWRNLTKYGTIVQFTYDDNGKTGRGYVRELDAPKELLDMLARAEGKLN'
'label':[ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2 ]}
```

The average  for the `seq` and the `label` are provided below:

| Feature    | Mean Count |
| ---------- | ---------------- |
| seq    | 256       |
| label (0) | 109            |
| label (1) | 54            |
| label (2) | 92            |

### 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}
}
```