proteinglm/ssp_q8

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 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:

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.

Source Data

Initial Data Collection and Normalization

The datasets applied in this study were originally published by NetSurfP-2.0.

Licensing Information

The dataset is released under the Apache-2.0 License.

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}
}