MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.

ABSTRACT: Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of hi

SUBMITTER: Liao H 

PROVIDER: S-EPMC10803737 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature