Poster Presentation 24th Annual Lorne Proteomics Symposium 2019

Recognising the role of post-translational modification in immunopeptidomic peptides by using mass spectrometry and bioinformatics (#124)

Shutao Mei 1 , Nathan P Croft 1 , Jiangning Song 1 , Anthony W Purcell 1
  1. Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia

The peptides modified by post-translational modification (PTM) then presented by the human leukocyte antigen (HLA) encoded molecules can be recognised by T cells as these modified peptides are not encoded by the human genome. The presence of such modified peptides in autoimmune diseases have been demonstrated can be associated with the development of some autoimune diseases, such as type 1 diabetes, celiac disease. Therefore, predicting PTM-peptides presented by HLA molecules is crucial for understanding the role of PTM-peptdies in the development of the autoimmune diseases. However, compared to the development of many PTM predictors that predict the PTM sites of proteins. So far, little is known about the characteristics of PTM-peptides in the immunopeptidome repertoire - i.e. the repertoire and sequences of PTM-peptides after proteolysis and presented by HLA class I (HLA-I) molecules.

In our work, we have found the deamidated peptides presented by HLA molecules show different characteristics compared to the deamidated peptides identified from proteolysis by using mass spectrometry. This finding may be helpful to better understand the role of deamidation in the development of some autoimmune diseases, such as celiac disease and type 1 diabetes. Moreover, predictor trained by deamidated peptides derived from immuno-peptidomics will be designed and built to predict deamidated peptides for further immunogenic screening.

This work aims to understand the characteristics of PTM-peptides presented by HLA molecules. Using mass spectrometry following bioinformatic works for building the next generation of PTM prediction models to screen immunogenic PTM peptides.