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---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 3068946
num_examples: 53614
- name: valid
num_bytes: 155744
num_examples: 2512
- name: test
num_bytes: 709292
num_examples: 12851
download_size: 2058102
dataset_size: 3933982
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- token-classification
tags:
- chemistry
- biology
size_categories:
- 10K<n<100K
---
# Dataset Card for Stability Stability Dataset
### Dataset Summary
The Stability Stability task is to predict the concentration of protease at which a protein can retain its folded state. Protease, being integral to numerous biological processes, bears significant relevance and a profound comprehension of protein stability during protease interaction can offer immense value, especially in the creation of novel therapeutics.
## Dataset Structure
### Data Instances
For each instance, there is a string representing the protein sequence and a float number indicating the stability score. See the [stability prediction dataset viewer](https://huggingface.co/datasets/Bo1015/stability_prediction/viewer) to explore more examples.
```
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':0.17}
```
The average for the `seq` and the `label` are provided below:
| Feature | Mean Count |
| ---------- | ---------------- |
| seq | 45 |
| label | 0.34 |
### Data Fields
- `seq`: a string containing the protein sequence
- `label`: a float number indicating the stability score of each sequence.
### Data Splits
The stability stability dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 53,614 |
| Valid | 2,512 |
| Test | 12,851 |
### Source Data
#### Initial Data Collection and Normalization
The dataset applied in this task is initially sourced from [Rocklin et al](https://www.science.org/doi/10.1126/science.aan0693) and subsequently collected within the [TAPE](https://github.com/songlab-cal/tape).
### 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}
}
``` |