Datasets:
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---
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}
}
``` |