metadata

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 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 secondary structure prediction 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 and subsequently collected within the TAPE.

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}
}