CIFTER: Automated charge-state determination for peptide tandem mass spectra

Abstract

Tandem mass spectrometry (MS/MS) has become a common and useful tool for analyzing complex protein mixtures. Database search programs are the most popular means for peptide identification from MS/MS spectra. However, estimations of charge states of peptide MS/MS spectra obtained from low-resolution mass spectrometers have not been reliable. They require repetitive database searches and additional analyses of the search results. We propose here an algorithm designed to reliably differentiate doubly charged spectra from triply charged ones. We conducted a rigorous analysis of various spectral features and their effects. We employed the distinguishing features found in our analysis and developed a classifier for multiply charged spectra using a machine learning approach. The test on various data sets showed that our method could be successfully applied independent of experimental setup and mass instrument. Ibis algorithm can be used to prefilter spectra so that only reasonably good spectra are submitted to database search programs, thereby saving considerable time.