proteinglm/peptide_HLA_MHC_affinity

Dataset Card for Peptide-HLA/MHC Affinity Dataset

Dataset Summary

The human leukocyte antigen (HLA) gene encodes major histo-compatibility complex (MHC) proteins, which can bind to peptide fragments and be presented to the cell surface for subsequent T cell receptors (TCRs) recognition. Accurately predicting the interaction between peptide sequence and HLA molecule will boost the understanding of immune responses, antigen presentation, and designing therapeutic interventions such as peptide-based vaccines or immunotherapies.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating that whether a given paired peptide and HLA sequence can bind or not. See the peptide-HLA/MHC affinity dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':1}

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

Feature	Mean Count
seq	45
label (0)	0.5
label (1)	0.5

Data Fields

• seq: a string containing the protein sequence
• label: an integer label indicating that whether a given paired peptide and HLA sequence can bind or not.

Data Splits

The Peptide-HLA/MHC Affinity dataset has 3 splits: train, valid, and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	57,357
Valid	7,008
Test	8,406

Source Data

Initial Data Collection and Normalization

The modeling data is from Wu et al. The raw dataset contains millions of samples, we used the same split and downsample 1% for training and 5% for validation and testing (57,357 training samples, 7,008 validation samples and 8,406 test samples).

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}
}