# De-motif sampling: an approach to decompose hierarchical motifs with applications in T cell recognition

## TL;DR

This paper introduces a new Bayesian method to better understand how T cell receptors recognize antigen peptides, independent of MHC constraints.

## Contribution

A novel Bayesian approach is introduced to decompose hierarchical motifs in T cell recognition, independent of MHC influence.

## Key findings

- The proposed Bayesian model successfully decomposes hierarchical motifs in T cell recognition.
- Simulation experiments and real data applications validate the model's effectiveness in capturing TCR-specific motifs.

## Abstract

T cell immune recognition requires the interactions among antigen peptides, Major Histocompatibility Complex (MHC) molecules, and T cell receptors (TCRs). While research into the interactions between MHC and peptides is well established, the specific preferences of TCRs for peptides remain less understood. This gap largely stems from the requirement that antigen peptides must be bound to MHC and presented on the cell surface prior to recognition by TCRs. Typically, motifs related to TCR recognition are influenced by MHC characteristics, limiting the direct identification of TCR-specific motifs. To address this challenge, this study introduces a Bayesian method designed to decompose hierarchical motifs independently of MHC constraints. This model, rigorously tested through comprehensive simulation experiments and applied to real data, establishes a clear hierarchical structure for motifs related to T cell recognition.