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EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.

Dani Korpela1, Emmi Jokinen1,2,3, Alexandru Dumitrescu1

  • 1Department of Computer Science, Aalto University, 02150 Espoo, Finland.

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Summary

A new machine learning model predicts T cell receptor (TCR) and peptide-MHC (pMHC) interactions, improving generalization for adaptive immunity and autoimmune disease research.

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Area of Science:

  • Immunology
  • Computational Biology
  • Machine Learning

Background:

  • T cells are crucial for adaptive immunity against pathogens and cancer, but aberrant T cell responses can cause autoimmune diseases.
  • T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is fundamental to initiating immune responses.
  • Predicting TCR-pMHC interactions is vital for understanding immune function and disease, yet current models face generalization challenges.

Purpose of the Study:

  • To develop an advanced machine learning model for predicting TCR-pMHC interactions.
  • To enhance the generalization capabilities of predictive models for TCR-pMHC binding.
  • To investigate the contribution of individual features and data augmentation strategies in TCR-pMHC prediction.

Main Methods:

  • Utilized ProtBERT embeddings for amino acid sequences of TCR alpha and beta chains, and epitopes.
  • Employed convolution and multi-head attention architectures to model complex interactions.
  • Incorporated epitope data with limited TCRs into training to improve model robustness.

Main Results:

  • The developed model demonstrates strong performance in predicting TCR-pMHC interactions.
  • Feature importance analysis highlights the significance of TCR chains, epitope, and MHC information.
  • Including sparse epitope data improved model generalization compared to existing state-of-the-art methods.

Conclusions:

  • The novel machine learning approach effectively predicts TCR-pMHC interactions, outperforming current models.
  • The model's ability to generalize to unseen pMHCs offers significant potential for immunological research.
  • This work provides a valuable tool for studying adaptive immunity, cancer immunology, and autoimmune diseases.