trimap-tools 1.0.5

Discovering TCR-Epitope-HLA interactions with Deep Learning

Discovering TCR-HLA-Epitope interactions with Deep Learning

trimap-tools is a package for analysis of peptide-HLA presentation and TCR specificity. It is designed to help researchers understand the interactions between T cell receptors (TCRs) and peptides presented by human leukocyte antigen (HLA) molecules, which play a crucial role in the immune response.

Installation

trimap-tools requires Python 3.9 or later. It is available on PyPI and can be installed using pip:

pip install trimap-tools

or by cloning the repository and installing it manually:

pip install git+https://github.com/uhlerlab/trimap-tools.git@main

Tutorials

For step-by-step guides on how to use trimap-tools, including training HLA/peptide encoders and predicting TCR specificity, please refer to the Documentation section.

Key Features

• Peptide representation learning with HLA context
  Learns latent embeddings of peptides while incorporating HLA background, enabling biologically informed modeling.

• TCR specificity prediction using full receptor sequences
  Supports comprehensive modeling of TCR recognition by leveraging both α and β chain CDR regions.

• Visualization of critical TCR residues
  Highlights key amino acid positions in TCRs that contribute to antigen recognition, aiding biological interpretation.

• Discovery of disease-associated epitopes
  Identifies novel peptides potentially involved in disease by integrating small-scale disease-specific data with large-scale public datasets.

Citation

If you use trimap-tools in your research, please cite the following paper: