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EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning.

Jaemin Jeon1, Suwan Yu1, Sangam Lee2

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea.

Bioinformatics (Oxford, England)
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

EpicPred identifies T-cell receptor (TCR) and epitope interactions specific to diseases. This method improves phenotype prediction in cancer and COVID-19 by filtering unlikely TCR-epitope pairs.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying T-cell receptor (TCR)-epitope interactions is crucial for understanding disease mechanisms and developing immunotherapies.
  • The CDR3 region of TCRs is key for epitope recognition, but profiling these interactions in disease contexts is underexplored.

Purpose of the Study:

  • To develop EpicPred, a computational tool for identifying phenotype-specific TCR-epitope interactions.
  • To enhance the prediction of disease phenotypes using TCR-epitope binding data.

Main Methods:

  • EpicPred utilizes Open-set Recognition (OSR) to filter improbable TCR-epitope interactions, reducing false positives.
  • Multiple instance learning is employed to pinpoint TCR-epitope interactions associated with specific cancer types or COVID-19 severity.

Main Results:

  • The study analyzed 244,552 TCR sequences and 105 epitopes from public databases.
  • EpicPred demonstrated superior performance in predicting phenotypes across cancer and COVID-19 datasets, achieving an average AUROC of 0.80 ± 0.07.
  • The method was validated on both bulk and single-cell resolution TCR sequencing data.

Conclusions:

  • EpicPred effectively identifies phenotype-specific TCR-epitope interactions.
  • The tool shows promise for advancing T-cell based diagnostics and therapeutics.
  • EpicPred is available as open-source software for further research.