Solving the spliced-peptides mystery by using machine learning techniques

Peptides bind to Human Leukocyte Antigens (p-HLA) and present on the cell surface work like a messenger to reports what is happening in the cell to the immune system. p-HLA derives from the intracellular proteins digestion. If a peptide sequence presented from a mutated region of the proteome (which could be the cause of cancer), then the immune system recognize the peptide on the cell surface and kills the tumor cell. For a long time, scientist believed that HLA-peptides, drive from just cutting proteins to small peptides by an enzyme called proteasome. However, recently it is discovered that proteasome not only cuts the proteins to peptides but also paste the resulted small peptides together and make new peptides called as “spliced peptides” which don’t have any template in the proteome. Understanding the rules of cutting and pasting peptides and prediction of spliced peptides sequences is a critical challenge in the “cancer vaccine design” field. We believe by using our empirical data and machine learning techniques; it is possible to discover splicing rules and ultimately predict spliced peptides sequences