| # Dataset Card for Secondary Structure Prediction (Q3) Dataset for RAGProtein | |
| ### 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 - Helix (includes alpha-helix, 3-10 helix, and pi helix), E - Strand (includes beta-strand and beta-bridge), C - Coil (includes turns, bends, and random coils). | |
| ## 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':'MRGSHHHHHHGSVKVKFVSSGEEKEVDTSKIKKVWRNLTKYGTIVQFTYDDNGKTGRGYVRELDAPKELLDMLARAEGKLN' | |
| 'label':[ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2 ] | |
| 'msa': 'MRGSHHHHHHGSVKVKFVSSGEEKEVDTSKIKKVWRNLTKYGTIVQFTYDDNGKTGRGYVRELDAPKELLDMLARAEGKLN|MRGSHHHHHHGSVKVKFVSSGEEKEVDTSKIKKVWRNLTKYGTIVQFTYDDNGKTGRGYVRELDAPKELLDMLARAEGKLN...', | |
| 'str_emb': [seq_len, 384] | |
| } | |
| ``` | |
| The average for the `seq` and the `label` are provided below: | |
| | Feature | Mean Count | | |
| | ---------- | ---------------- | | |
| | seq | 256 | | |
| | label (0) | 109 | | |
| | label (1) | 54 | | |
| | label (2) | 92 | | |
| ### Data Fields | |
| - `seq`: a string containing the protein sequence | |
| - `label`: a sequence containing the structural label of each residue. | |
| - `msa`: "|" seperated MSA sequences | |
| - `str_emb`: AIDO.StructureTokenizer generated structure embedding from AF2 predicted structures | |
| ### 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). | |
| ### Processed data collection | |
| Single sequence data are collected from this 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} | |
| } | |
| ``` |