metadata

dataset_info:
  features:
    - name: seq
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 986536
      num_examples: 19526
    - name: test
      num_bytes: 227922
      num_examples: 4485
  download_size: 458823
  dataset_size: 1214458
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - text-classification
tags:
  - chemistry
  - biology
size_categories:
  - 10K<n<100K

Dataset Card for TCR_pMHC_Affinity Dataset

Dataset Summary

The interaction between T cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) plays a crucial role in the recognition and activation of T cells in the immune system. TCRs are cell surface receptors found on T cells, and pMHCs are complexes formed by peptides derived from antigens bound to major histocompatibility complexes (MHCs) on the surface of antigen-presenting cells. The classification task is to predict whether a given paired TCR sequence and peptide can bind or not.

Dataset Structure

Data Instances

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

{'seq':'CAGADGGSQGNLIF|CASSTRSTDTQYF|GILGFVFTL'
'label':1}

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

Feature	Mean Count
seq	39
label (0)	0.83
label (1)	0.17

Data Fields

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

Data Splits

The TCR_pMHC_Affinity dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	19,526
Test	4,485

Source Data

Initial Data Collection and Normalization

The dataset is major from VDJdb, processed and curated from epiTCR.

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}
}