Datasets:

YYJMAY
/

pt-recognition

language:
  - en
tags:
  - biology
  - immunology
  - tcr
  - peptide
  - t-cell-receptor
  - binding-prediction
task_categories:
  - text-classification
size_categories:
  - 10K<n<100K

PT Interaction Dataset

Dataset Description

The PT (Peptide-TCR) interaction dataset is designed for training and evaluating T-Cell Receptor (TCR) binding prediction models with full TCR sequence information. This dataset contains paired peptide sequences and complete TCR alpha/beta chain sequences (including all 6 CDR regions: A1-A3, B1-B3), along with binary binding labels.

Key Features

Full TCR Information: Contains all 6 CDR regions (A1, A2, A3, B1, B2, B3) for both alpha and beta chains
Binary Labels: Binding labels (0=non-binder, 1=binder)
HLA Allele Information: MHC allele context for each peptide-TCR pair
Peptide Length Range: 8-12 amino acids
CDR3β Length Range: 5-23 amino acids
Training Set: 43,378 samples (13.62% positive, 86.38% negative)
Test Set: 2,956 samples (13.97% positive, 86.03% negative)

Dataset Statistics

Split	Samples	Positives	Negatives	Unique TCRs	Unique HLAs
Train	43,378	5,906 (13.62%)	37,472 (86.38%)	10,414	10
ID Test	2,956	413 (13.97%)	2,543 (86.03%)	2,511	10

Data Format

Each row contains the following columns:

peptide: Amino acid sequence of the peptide (8-12 aa)
A1, A2, A3: CDR1α, CDR2α, CDR3α sequences
B1, B2, B3: CDR1β, CDR2β, CDR3β sequences
binder: Binary binding label (0=non-binder, 1=binder)
allele: HLA allele (e.g., A02:01, B07:02)

Example Data

{
    "peptide": "KLGGALQAK",
    "A1": "SSVPPY",
    "A2": "YTSAATLV",
    "A3": "AVKWSSNYKLT",
    "B1": "SQVTM",
    "B2": "ANQGSEA",
    "B3": "SVGSGDHGEQF",
    "binder": 0,
    "allele": "A*03:01"
}

Dataset Construction

Data Sources

The PT dataset is curated from multiple publicly available TCR-peptide binding databases and experimental studies, including:

VDJdb: A curated database of T-cell receptor sequences
McPAS-TCR: Manually curated catalog of pathology-associated TCR sequences
IEDB: Immune Epitope Database
Published experimental validation studies

Quality Control

TCR Leakage Prevention: Train and test splits are carefully constructed to ensure no TCR overlap based on CDR3β sequences
Duplicate Removal: All duplicate (peptide, B3, binder) combinations are removed
Length Filtering: Only peptides of length 8-12 amino acids are included
HLA Standardization: All HLA alleles follow the format "A*02:01" (without "HLA-" prefix)
Data Validation: All sequences are validated for amino acid composition

Split Strategy

ID Test: Random split preserving the same peptide/HLA/TCR distribution as training
No TCR Leakage: Train and test sets are strictly disjoint based on CDR3β sequences

Usage

Loading the Dataset

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("YYJMAY/pt-interaction")

# Access splits
train_data = dataset['train']
test_data = dataset['test']

# Convert to pandas DataFrame
import pandas as pd
train_df = pd.DataFrame(train_data)
test_df = pd.DataFrame(test_data)

Training Example

from datasets import load_dataset
import pandas as pd

# Load training data
dataset = load_dataset("YYJMAY/pt-interaction", split="train")
df = pd.DataFrame(dataset)

# Prepare features
X = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
y = df['binder']

# Train your model
# model.fit(X, y)

Evaluation Example

from datasets import load_dataset
import pandas as pd
from sklearn.metrics import roc_auc_score, accuracy_score

# Load test data
dataset = load_dataset("YYJMAY/pt-interaction", data_files="id_test.csv")
df = pd.DataFrame(dataset['train'])  # HF loads single files as 'train' split

# Make predictions
X_test = df[['peptide', 'A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'allele']]
y_test = df['binder']

# predictions = model.predict(X_test)
# print(f"AUC: {roc_auc_score(y_test, predictions):.4f}")
# print(f"Accuracy: {accuracy_score(y_test, predictions > 0.5):.4f}")

Citation

If you use this dataset in your research, please cite:

@misc{pt_interaction_dataset,
  title={PT Interaction Dataset: Peptide-TCR Binding Prediction},
  author={SPRINT Benchmark Contributors},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/YYJMAY/pt-interaction}}
}

Related Datasets

PM Dataset: Peptide-MHC binding (no TCR information)
PMT Dataset: Peptide-MHC-TCR with CDR3β only
Allelic OOD: Out-of-distribution test for rare HLA alleles
Temporal OOD: Out-of-distribution test for COVID-19 era data
Modality OOD: Cross-modality generalization (BA vs EL)

License

This dataset is released under the MIT License. The original data sources may have their own licenses.

Contact

For questions or issues, please open an issue on the SPRINT GitHub repository.

Dataset Card Authors

SPRINT Benchmark Team

Dataset Version

Version: 1.0
Last Updated: 2025-01-19