proteinglm/enzyme_catalytic_efficiency

Dataset Card for Enzyme Catalytic Efficiency Dataset

Dataset Summary

This task is focused on predicting $k_cat$ values, which are enzymatic turnover numbers denoting the maximum chemical conversion rate of a reaction, for metabolic enzymes originating from any organism. These predictions are based on substrate structures and protein sequences. The underlying importance of this task lies in its potential to yield high-throughput and accurate $k_cat$ predictions applicable to any organism or enzyme. Such capabilities are crucial for advancing our understanding of cellular metabolism and physiology.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and a float value indicating the $k_cat$ score of the protein sequence. See the enzyme catalytic efficiency dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':3.6}

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

Feature	Mean Count
seq	418
label	1.87

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 Enzyme Catalytic Efficiency dataset has 3 splits: train, valid and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	13,470
Valid	1,684
Test	1,684

Source Data

Initial Data Collection and Normalization

The data, sourced from a variety of repositories including BRENDA, SABIO-RK, KEGG, UniProt, and MetaCyc, are curated by Li et al.

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}
}