RHybridFinder: An R package to process immunopeptidomic data for putative hybrid peptide discovery

Abstract

Summary Identification of proteasomal spliced peptides (PSPs) by mass spectrometry (MS) is not possible with traditional search engines. Here, we provide a protocol for running RHybridFinder (RHF), an R package for the computational inference of putative PSPs detected by MS. RHF extracts high confidence scored de novo sequenced peptides identified by PEAKS software. Those peptides are then matched to protein databases to infer cis- or trans-spliced major histocompatibility complex (MHC)-associated peptides. RHF is relatively fast and straightforward. PSPs have to be validated experimentally. For complete details on the use and execution of the original protocol, please refer to Faridi et al. (2018) .

Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines

“…This has relevance in the context of SARS-COV-2 variants since the SARS-CoV-2 Omicron BA.1 spike G446S mutation, located just outside the N-terminus of a cognate CD8+ Tcell epitope, was recently shown to improve antigen processing/presentation and antiviral T-cell recognition through tripeptidyl peptidase II (TPPII), a post-proteasomal protease that mediates antigen processing 97 . Furthermore, MHCvalidator, by learning and incorporating rules and predictions of cryptic peptides, including polypeptides created by posttranslational peptide splicing [98][99][100][101][102] , could become a valuable tool for discriminating between true and false spliced peptides without the need for additional experiments to validate their identifications. If further developed and tested, MHCvalidator could therefore be applied to continue the development of databases dedicated to immunopeptidomics, such as SysteMHC Atlas [103][104][105] .…”