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proteinglm/cloning_clf

Dataset Card for Cloning CLF Dataset Dataset Summary Protein structure determination includes a series of experimental stages to yield stable proteins for X-ray crystallography. Specifically, the proteins are first selected and expressed, then purified for crystal structure determination. Each step corresponds to a "stage tag" to denote whether the protein is stable under a certain stage. Dataset Structure Data Instances For each… See the full description on the dataset page: https://huggingface.co/datasets/proteinglm/cloning_clf.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Dataset Card

Dataset Card for Cloning CLF Dataset

Dataset Summary

Protein structure determination includes a series of experimental stages to yield stable proteins for X-ray crystallography. Specifically, the proteins are first selected and expressed, then purified for crystal structure determination. Each step corresponds to a "stage tag" to denote whether the protein is stable under a certain stage.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating whether a protein sequence is stable under a certain stage. See the Cloning CLF dataset viewer to explore more examples.

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

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

FeatureMean Count
seq315
label (0)0.6
label (1)0.4

Data Fields

  • —seq: a string containing the protein sequence
  • —label: a float value indicating the $k_cat$ score of the protein sequence.

Data Splits

The cloning clf dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset SplitNumber of Instances in Split
Train23,375
Test4,791

Source Data

Initial Data Collection and Normalization

The dataset is collected from PredPPCrys, which manually annotated thousands of proteins with different experimental procedures.

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}
}