genbio-ai/fold_prediction_rag

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 sequences
• str_emb: AIDO.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}
}