metadata

dataset_info:
  features:
    - name: seq
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 88951983
      num_examples: 283057
    - name: valid
      num_bytes: 19213838
      num_examples: 62973
    - name: test
      num_bytes: 22317993
      num_examples: 73205
  download_size: 127753697
  dataset_size: 130483814
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:
  - text-classification
tags:
  - chemistry
  - biology
size_categories:
  - 100K<n<1M

Dataset Card for Temperature Stability Dataset

Dataset Summary

The accurate prediction of protein thermal stability has far-reaching implications in both academic and industrial spheres. This task primarily aims to predict a protein’s capacity to preserve its structural stability under a temperature condition of 65 degrees Celsius.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating whether the protein can maintain its structural stability at a temperature of 65 degrees Celsius. See the temperature stability dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':1}

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

Feature	Mean Count
seq	300

Data Fields

seq: a string containing the protein sequence
label: an integer label indicating the structural stability of each sequence.

Data Splits

The temperature stability dataset has 3 splits: train, valid, and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	283,057
Valid	62,973
Test	73,205

Source Data

Initial Data Collection and Normalization

We adapted the dataset strategy from TemStaPro.

Licensing Information

The dataset is released under the Apache-2.0 License.

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}
}