An iterative strategy for precursor ion selection for LC-MS/MS based shotgun proteomics

Abstract

Currently, the precursor ion selection strategies in LC-MS mainly choose the most prominent peptide signals for MS/MS analysis. Consequently, high abundant proteins are identified by MS/MS of many peptides whereas proteins of lower abundance might elude identification. We present a novel, iterative and result-driven approach for precursor ion selection that significantly increases the efficiency of an MS/MS analysis by decreasing data redundancy and analysis time. By simulating different strategies for precursor ion selection on an existing dataset we compare our method to existing result-driven strategies and evaluate its performance with regard to mass accuracy, database size, and sample complexity.