Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation.

Abstract

Negative electron-transfer dissociation (NETD) has emerged as a premier tool for peptide anion analysis, offering access to acidic post-translational modifications and regions of the proteome that are intractable with traditional positive-mode approaches. Whole-proteome scale characterization is now possible with NETD, but proper informatic tools are needed to capitalize on advances in instrumentation. Currently only one database search algorithm (OMSSA) can process NETD data. Here we implement NETD search capabilities into the Byonic platform to improve the sensitivity of negative-mode data analyses, and we benchmark these improvements using 90 min LC-MS/MS analyses of tryptic peptides from human embryonic stem cells. With this new algorithm for searching NETD data, we improved the number of successfully identified spectra by as much as 80% and identified 8665 unique peptides, 24 639 peptide spectral matches, and 1338 proteins in activated-ion NETD analyses, more than doubling identifications from previous negative-mode characterizations of the human proteome. Furthermore, we reanalyzed our recently published large-scale, multienzyme negative-mode yeast proteome data, improving peptide and peptide spectral match identifications and considerably increasing protein sequence coverage. In all, we show that new informatics tools, in combination with recent advances in data acquisition, can significantly improve proteome characterization in negative-mode approaches.