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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: int64 |
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splits: |
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- name: train |
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num_bytes: 1875591 |
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num_examples: 6000 |
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- name: test |
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num_bytes: 480997 |
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num_examples: 1332 |
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download_size: 2310262 |
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dataset_size: 2356588 |
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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: 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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--- |
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# Dataset Card for Metal Ion Binding Dataset |
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### Dataset Summary |
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Metal ion binding sites within proteins play a crucial role across a spectrum of processes, spanning from physiological to pathological, toxicological, pharmaceutical, and diagnostic. Consequently, the development of precise and efficient methods to identify and characterize these metal ion binding sites in proteins has become an imperative and intricate task for bioinformatics and structural biology. |
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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 an integer label indicating the existence of metal-ion binding site(s) on a given protein sequence. See the [metal ion binding dataset viewer](https://huggingface.co/datasets/Bo1015/metal_ion_binding/viewer) to explore more examples. |
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``` |
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{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
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'label':1} |
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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 | 309 | |
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| label (0) | 0.5 | |
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| label (1) | 0.5 | |
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### Data Fields |
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- `seq`: a string containing the protein sequence |
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- `label`: an integer label indicating the existence of metal-ion binding site(s) on a given protein sequence |
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### Data Splits |
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The metal ion binding dataset has 2 splits: _train_ 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 | 6,000 | |
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| Test | 1,332 | |
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### Source Data |
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#### Initial Data Collection and Normalization |
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We employ data collected from [Cheng et al](https://www.nature.com/articles/s41589-022-01223-z) curated from the Protein Data Bank (PDB). |
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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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``` |
