genbio-ai/fluorescence_prediction_rag

Dataset Card for Fluorescence Prediction Dataset for RAGProtein

Dataset Summary

The Fluorescence Prediction task focuses on predicting the fluorescence intensity of green fluorescent protein mutants, a crucial function in biology that allows researchers to infer the presence of proteins within cell lines and living organisms. This regression task utilizes training and evaluation datasets that feature mutants with three or fewer mutations, contrasting the testing dataset, which comprises mutants with four or more mutations.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and a float value indicating the fluorescence score of the protein sequence. See the fluorescence prediction dataset viewer to explore more examples.

{
'seq':    'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':   3.6,
'msa':     'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL|MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL...',
'str_emb': [seq_len, 384]
}

The average for the seq and the label are provided below:

Feature	Mean Count
seq	237
label	2.63

Data Fields

• seq: a string containing the protein sequence
• label: a float value indicating the fluorescence score of the protein sequence.
• msa: "|" seperated MSA sequences
• str_emb: AIDO.StructureTokenizer generated structure embedding from AF2 predicted structures

Data Splits

The fluorescence prediction dataset has 3 splits: train, valid and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	21,446
Valid	5,362
Test	27,217

Source Data

Initial Data Collection and Normalization

The datasets is collected from the TAPE.

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}
}