## 🚀 Key Features of `trimap-tools`
- **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.
## 🔧 Components
- `trimap.model.HLA_vae` — Variational Autoencoder for HLA sequences
- `trimap.model.PEP_vae` — Variational Autoencoder for peptide sequences
- `trimap.model.THE` — Main prediction module integrating TCRs with peptide or HLA targets
## 📁 Sections
- **Tutorials**
Step-by-step guides for training HLA/peptide encoders and predicting TCR specificity using example or custom data.
- **Discover Disease-Associated Epitopes**
Learn how to integrate small-scale experimental data with large public datasets to uncover novel epitope–TCR interactions relevant to specific diseases.
Explore the sidebar to get started 👉
For installation instructions, pretrained models, or benchmarking datasets, refer to the corresponding sections.