Datasets:
| dataset_info: | |
| features: | |
| - name: seq | |
| dtype: string | |
| - name: label | |
| sequence: int64 | |
| splits: | |
| - name: train | |
| num_bytes: 24941535 | |
| num_examples: 10848 | |
| - name: test | |
| num_bytes: 1665908 | |
| num_examples: 667 | |
| download_size: 3931715 | |
| dataset_size: 26607443 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: test | |
| path: data/test-* | |
| license: apache-2.0 | |
| task_categories: | |
| - token-classification | |
| tags: | |
| - chemistry | |
| - biology | |
| size_categories: | |
| - 10K<n<100K | |
| # Dataset Card for Secondary Structure Prediction (Q8) Dataset | |
| ### Dataset Summary | |
| The study of a protein’s secondary structure (Sec. Struc. P.) forms a fundamental cornerstone in understanding its biological function. This secondary structure, comprising helices, strands, and various turns, bestows the protein with a specific three-dimensional configuration, which is critical for the formation of its tertiary structure. In the context of this work, a given protein sequence is classified into three distinct categories, each representing a different structural element: H - Alpha-helix, G - 3-10 helix, I - Pi helix, E - Beta-strand, B - Beta-bridge, T - Turn, S - Bend, C - Coil (or random coil). | |
| ## Dataset Structure | |
| ### Data Instances | |
| For each instance, there is a string of the protein sequences, a sequence for the strucutral labels. See the [Secondary structure prediction dataset viewer](https://huggingface.co/datasets/Bo1015/ssp_q8/viewer/default/test) to explore more examples. | |
| ``` | |
| {'seq':'GKITFYEDRGFQGRHYECSSDHSNLQPYFSRCNSIRVDSGCWMLYEQPNFQGPQYFLRRGDYPDYQQWMGLNDSIRSCRLIPHTGSHRLRIYEREDYRGQMVEITEDCSSLHDRFHFSEIHSFNVLEGWWVLYEMTNYRGRQYLLRPGDYRRYHDWGATNARVGSLRRAVDFY' | |
| 'label':[ 7, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 7, 6, 6, 6, 7, 5, 7, 7, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 7, 5, 5, 5, 7, 7, 7, 4, 4, 4, 4, 4, 7, 7, 7, 5, 7, 7, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 5, 6, 6, 6, 5, 5, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 5, 7, 4, 4, 4, 4, 5, 5, 5, 5, 5, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 7, 5, 0, 0, 0, 0, 6, 7, 5, 5, 7, 7, 7, 7, 4, 4, 4, 4, 7, 7, 7, 7, 7 ]} | |
| ``` | |
| The average for the `seq` and the `label` are provided below: | |
| | Feature | Mean Count | | |
| | ---------- | ---------------- | | |
| | seq | 256 | | |
| | label (0) | 9 | | |
| | label (1) | 82 | | |
| | label (2) | 1 | | |
| | label (3) | 3 | | |
| | label (4) | 52 | | |
| | label (5) | 26 | | |
| | label (6) | 19 | | |
| | label (7) | 63 | | |
| ### Data Fields | |
| - `seq`: a string containing the protein sequence | |
| - `label`: a sequence containing the structural label of each residue. | |
| ### Data Splits | |
| The secondary structure prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset. | |
| | Dataset Split | Number of Instances in Split | | |
| | ------------- | ------------------------------------------- | | |
| | Train | 10,848 | | |
| | Test | 667 | | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| The datasets applied in this study were originally published by [NetSurfP-2.0](https://pubmed.ncbi.nlm.nih.gov/30785653/). | |
| ### 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} | |
| } | |
| ``` |