---
dataset_info:
  features:
  - name: seq
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 3238298
    num_examples: 57357
  - name: valid
    num_bytes: 395504
    num_examples: 7008
  - name: test
    num_bytes: 474618
    num_examples: 8406
  download_size: 1494430
  dataset_size: 4108420
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: valid
    path: data/valid-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- text-classification
tags:
- chemistry
- biology
size_categories:
- 10K<n<100K
---


# 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](https://huggingface.co/datasets/Bo1015/peptide_HLA_MHC_affinity/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](https://www.biorxiv.org/content/10.1101/2023.04.24.538196v1). 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](http://www.apache.org/licenses/LICENSE-2.0). 

### 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}
}
```