Improved data-driven bioinformatics tools to greatly extend neo- and xeno-epitope landscapes detected by immunopeptidomics.

01 November 2021 → 31 October 2025
Research Foundation - Flanders (FWO)
Research disciplines
  • Natural sciences
    • Machine learning and decision making
    • Development of bioinformatics software, tools and databases
  • Medical and health sciences
    • Computational biomodelling and machine learning
    • Bio-informatics and computational biology not elsewhere classified
Bioinformatics Clinical proteomics Machine Learning
Project description

Vaccination has proven to be very successful, resulting in the eradication of smallpox and the near-eradication of poliovirus, for example. Currently, vaccination is available for over 29 diseases and is estimated to prevent over 3 million deaths a year. However, for some diseases such as cancer and tuberculosis, effective vaccines are not yet available. A major problem in developing vaccines for diseases such as cancer and those caused by intracellular bacteria is that these must rely heavily on T-cell immunity, which requires the identification of efficiently presented MHC-epitopes that will elicit a potent immune response in the body. The identification of such MHC-bound epitopes is pursued most directly through immunopeptidomics, in which the bound epitopes are isolated and analyzed. However, while new mass spectrometry-based protocols are being designed to increase the sensitivity of the experimental identification of these epitopes, including in the lab of my co-promotor Prof. Impens, bioinformatics tools that can efficiently identify the resulting fragmentation mass spectra lag behind. I here therefore will develop novel bioinformatics tools that are specifically tailored to work with such immunopeptidomics data. A key outcome of this effort will be to provide a more comprehensive view on the available epitopes for vaccination efforts, which can help overcome current limitations in searching for applicable epitopes for cancer and intracellular bacterial infections.