In Journal of proteome research
Targeted and semitargeted mass spectrometry-based approaches are reliable methods to consistently detect and quantify low abundance proteins including proteins of clinical significance. Despite their potential, the development of targeted and semitargeted assays is time-consuming and often requires the purchase of costly libraries of synthetic peptides. To improve the efficiency of this rate-limiting step, we developed PeptideRanger, a tool to identify peptides from protein of interest with physiochemical properties that make them more likely to be suitable for mass spectrometry analysis. PeptideRanger is a flexible, extensively annotated, and intuitive R package that uses a random forest model trained on a diverse data set of thousands of MS experiments spanning a variety of sample types profiled with different chromatography setups and instruments. To support a variety of applications and to leverage rapidly growing public MS databases, PeptideRanger can readily be retrained with experiment-specific data sets and customized to prioritize and filter peptides based on selected properties.
Riley Ryan M, Spencer Miko Sandra E, Morin Ryan D, Morin Gregg B, Negri Gian Luca
2023-Jan-26
machine learning, mass spectrometry, peptide detectability, proteomics
