proteinglm/fitness_prediction

Dataset Card for Fitness Prediction (GB1) Dataset

Dataset Summary

The Fitness Prediction (GB1) task is dedicated to anticipating the fitness landscape of the GB1 domain, a process that plays a pivotal role in understanding and enhancing the binding affinity and stability of this domain. As a prevalent protein interaction domain, GB1 finds wide usage in diverse applications such as protein purification, biosensors, and drug delivery. This task is configured as a regression problem, where the goal is to predict the fitness score of GB1 binding following mutations at four specific positions.

Dataset Structure

Data Instances

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

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':3.6}

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

Feature	Mean Count
seq	265
label	1.12

Data Fields

• seq: a string containing the protein sequence
• label: a float value indicating the fitness score of the protein sequence.

Data Splits

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

Dataset Split	Number of Instances in Split
Train	6,289
Valid	699
Test	1,745

Source Data

Initial Data Collection and Normalization

The data for this task is sourced from the FLIP database.

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}
}