Determination of immunogenic epitopes

Abstract

The identification of immunogenic epitopes (such as fragments of proteins, in particular peptides, that can trigger an immune response) is a fundamental need for immune-based therapies. A computational tool that could detect such immunogenic epitopes would have vast potential applications in biomedicine ranging, from vaccine design against viruses or bacteria to therapeutic vaccination of cancer patients. While there are several methods that predict whether a peptide will be shown to the immune system via the HLA proteins of a patient, most of them cannot predict whether such presentation will indeed trigger an immune response. The aim of this project is to build an immunogenicity predictor that discriminates immunogenic from non-immunogenic epitopes. After a careful study of the drivers of antigen processing and presentation on HLA class I molecules and an assessment of the physicochemical factors influencing epitope recognition by T-cell receptors (TCRs), we have used a selection of publicly available tools and in-house developed algorithms to identify the most relevant features that determine epitope immunogenicity. We then used these features to build an immunogenicity predictor (PredIG) modelled by logistic regression against immunogenically validated epitopes by the ImmunoEpitope DataBase (IEDB). Overall, our immunogenicity predictor shows a better performance in identifying immunogenic epitopes than other stateof- the-art metrics.