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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 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 sequencelabel
: a sequence containing the structural label of each residue.msa
: "|" seperated MSA sequencesstr_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.
Licensing Information
The dataset is released under the Apache-2.0 License.
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}
}
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