In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides.

Swantje Lenz,Juri Rappsilber,Sven H Giese,Lutz Fischer

doi:10.1021/acs.jproteome.8b00600

Abstract

Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by ‘”in-search assignment of the monoisotopic peak” in the cross-link database search tool Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available data sets. Xi-MPA always delivered the highest number of identifications with ∼2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

Highlights

We show that standard software suites, MaxQuant and OpenMS correct monoisotopic precursor masses of cross-linked peptides with variable success
We evaluated the impact on cross-link identification in Xi of changing the precursor monoisotopic mass that was initially assigned during data acquisition (“uncorrected”)
The newly implemented in-search assignment of monoisotopic peaks in Xi was compared to the elaborate preprocessing pipelines in OpenMS and MaxQuant

Summary

Introduction

Several approaches have been utilized to increase the numbers of identified cross-links, for example enriching for cross-linked peptides,[1−4] using different proteases[1,5,6] or optimizing fragmentation methods.[7,8] In parallel with experimental developments, data analysis has progressed to extract even more cross-links to be used as distance restraints for modeling of proteins and their complexes.[9,10] Search software has been designed for the identification of cross-linked peptides, for example Kojak,[11] xQuest,[12] pLink,[13] XlinkX,[14] or Xi.[5]. Established proteomics software perform such preprocessing, including MaxQuant[20,21] and OpenMS.[22,23] For example, MaxQuant performs a variety of preprocessing steps: it corrects the precursor m/z by an intensity-weighted average if a suitable peptide feature is found, reassigns the monoisotopic peak and contains options for intensity filtering of MS2 peaks. Despite such correction of the precursor mass, many linear search engines have integrated the possibility of considering multiple monoisotopic peaks during search.[24−26] the benefits of this search feature are currently unclear. It seems that the assignment of monoisotopic mass for tryptic peptides is already achieved adequately either during acquisition or as part of preprocessing

Methods

Results

Conclusion