GB.Structure
Collection
14 items • Updated
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: RuntimeError
Message: Dataset scripts are no longer supported, but found fold_prediction_rag.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1177, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found fold_prediction_rag.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Dataset Card for Fold Prediction Dataset for RAGProtein
Dataset Summary
Fold class prediction is a scientific classification task that assigns protein sequences to one of 1,195 known folds. The primary application of this task lies in the identification of novel remote homologs among proteins of interest, such as emerging antibiotic-resistant genes and industrial enzymes. The study of protein fold holds great significance in fields like proteomics and structural biology, as it facilitates the analysis of folding patterns, leading to the discovery of remote homologies and advancements in disease research.
Dataset Structure
Data Instances
For each instance, there is a string representing the protein sequence and an integer label indicating which know fold a protein sequence belongs to. See the fold prediction dataset viewer to explore more examples.
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':6,
'msa': 'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL|MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL...',
'str_emb': [seq_len, 384]
}
The average for the seq and the label are provided below:
| Feature | Mean Count |
|---|---|
| seq | 168 |
Data Fields
seq: a string containing the protein sequence.label: an integer label indicating which know fold a protein sequence belongs to.msa: "|" seperated MSA sequencesstr_emb: GB.StructureTokenizer generated structure embedding from AF2 predicted structures
Data Splits
The fold prediction dataset has 3 splits: train, valid and test. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
|---|---|
| Train | 12,312 |
| Valid | 736 |
| Test | 3,244 |
Source Data
Initial Data Collection and Normalization
The dataset employed for this task is based on SCOP 1.75, a release from 2009.
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}
}
- Downloads last month
- 93