| | --- |
| | 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](https://huggingface.co/datasets/Bo1015/stability_prediction/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 stability stability 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](https://www.science.org/doi/10.1126/science.aan0693) and subsequently collected within the [TAPE](https://github.com/songlab-cal/tape). |
| |
|
| | ### Licensing Information |
| |
|
| | The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). |
| |
|
| | ### 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} |
| | } |
| | ``` |
