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DECODE: a computational pipeline to discover T cell receptor binding rules.

Iliana Papadopoulou1,2, An-Phi Nguyen1,3, Anna Weber1,2

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DECODE is a new computational pipeline that extracts binding rules from T cell receptor (TCR) prediction models. This tool enhances understanding of TCR-epitope interactions and aids in developing safer immunotherapies.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding T cell receptor (TCR) binding mechanisms is crucial for adaptive immunity and designing T cell-based therapies.
  • Machine learning models predict TCR binding but often act as "black boxes," limiting insight into binding rules.
  • Millions of TCR sequences from repertoire sequencing fuel computational model development.

Purpose of the Study:

  • To present DECODE, a customizable computational pipeline for extracting binding rules from TCR-epitope prediction models.
  • To provide analytical and visualization tools to aid users in rule extraction.
  • To demonstrate DECODE's utility in understanding TCR binding motifs and immunotherapeutic challenges.

Main Methods:

  • Developed a computational pipeline named DECODE.
  • Integrated analytical and visualization tools for rule extraction.
  • Applied DECODE to the TITAN TCR-binding prediction model for demonstration.

Main Results:

  • DECODE successfully extracts binding rules from black-box TCR prediction models.
  • The pipeline provides metrics to assess the quality of extracted rules.
  • Demonstrated DECODE's ability to reveal sequence motifs underlying TCR binding.

Conclusions:

  • DECODE enhances the understanding of sequence motifs governing TCR-epitope binding.
  • The pipeline can help investigate immunotherapeutic challenges like off-target TCR binding and cross-reactivity.
  • DECODE facilitates the development of more effective and safer T cell-based therapies.