Felix Drost
PhD candidate


Dissecting the T-Cell Receptor-Epitope Landscape through Semisupervised Deep Learning

My work revolves around T cell receptors (TCRs). T cells recognize epitopes (= small parts of viruses, tumors, and self-antigens) via their TCR - a highly variable cell surface protein - to eliminate harmful or infected cells. Understanding the interaction between TCR-epitope will provide us with fundamental insights into the adaptive immune system for developing immunotherapies, and vaccines. During my PhD, I am developing machine learning based models predicting the TCR-epitope interaction based on their protein sequences. Ultimatively, such models will enable us to analyze enormous receptor reqertoires computationally within the blink of an eye without laborious experimental testing.

Research interests

  • Adaptive Immunology, esp. TCR-epitope interaction
  • Applied Deep Learning for Bioinformatics
  • Sequence learning (e.g. Proteins, DNA / RNA, NLP)
  • Single-cell analysis


Shoot me a short mail including your CV, transcript, and a short description of one of your projects, if you are interested in doing a project on one of the following topics:

  • Developing deep learning tools for predicting the TCR-epitope recognition
  • Integrating multi-modal data, esp. sequences (TCR) and count data (Genes)
  • Analysis of single cell T cell datasets in the context of vaccines, immunotherapies, and diseases