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--- |
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dataset_info: |
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features: |
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- name: seq |
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dtype: string |
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|
- name: label |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 5777549 |
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num_examples: 13470 |
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|
- name: valid |
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num_bytes: 735028 |
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num_examples: 1684 |
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|
- name: test |
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|
num_bytes: 723264 |
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num_examples: 1684 |
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download_size: 6562046 |
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dataset_size: 7235841 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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- split: test |
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path: data/test-* |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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tags: |
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- chemistry |
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- biology |
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- medical |
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size_categories: |
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- 10K<n<100K |
|
|
--- |
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# Dataset Card for Enzyme Catalytic Efficiency Dataset |
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### Dataset Summary |
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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. |
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## Dataset Structure |
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### Data Instances |
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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](https://huggingface.co/datasets/Bo1015/enzyme_catalytic_efficiency/viewer) to explore more examples. |
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``` |
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{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
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'label':3.6} |
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``` |
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The average for the `seq` and the `label` are provided below: |
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| Feature | Mean Count | |
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| ---------- | ---------------- | |
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| seq | 418 | |
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| label | 1.87 | |
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### Data Fields |
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- `seq`: a string containing the protein sequence |
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- `label`: a float value indicating the $k_cat$ score of the protein sequence. |
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### Data Splits |
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The Enzyme Catalytic Efficiency dataset has 3 splits: _train_, _valid_ and _test_. Below are the statistics of the dataset. |
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| Dataset Split | Number of Instances in Split | |
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| ------------- | ------------------------------------------- | |
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| Train | 13,470 | |
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| Valid | 1,684 | |
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| Test | 1,684 | |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The data, sourced from a variety of repositories including BRENDA, SABIO-RK, KEGG, UniProt, and MetaCyc, are curated by [Li et al](https://www.nature.com/articles/s41929-022-00798-z). |
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### Licensing Information |
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The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). |
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### Citation |
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If you find our work useful, please consider citing the following paper: |
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``` |
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@misc{chen2024xtrimopglm, |
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title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein}, |
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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}, |
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year={2024}, |
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eprint={2401.06199}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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note={arXiv preprint arXiv:2401.06199} |
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} |
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``` |
