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TAPIR: a T-cell receptor language model for predicting rare and novel targets
Ethan Fast1, Manjima Dhar1, Binbin Chen1
1Vcreate, Inc., Menlo Park, CA, 94025, USA.
Biorxiv : the Preprint Server for Biology
|September 25, 2023
Summary
We developed TAPIR, a T-cell receptor (TCR) language model, to predict TCR-target interactions, even for novel targets. TAPIR accurately identifies anti-cancer TCRs and can design new TCR sequences for therapeutic applications.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- T-cell receptors (TCRs) play a crucial role in various diseases, but predicting their specific targets is a significant challenge.
- Understanding TCR-target interactions is vital for developing targeted immunotherapies.
Conclusions:
- TAPIR is a powerful tool for predicting TCR-target interactions, advancing the field of immunoinformatics.
- The model's ability to handle novel targets and design new TCR sequences opens new avenues for cancer immunotherapy development.
- TAPIR's flexibility and performance offer significant potential for personalized medicine and drug discovery.
