Cross-linking mass spectrometry has become a powerful
tool for
the identification of protein–protein interactions and for
gaining insight into the structures of proteins. We previously published
MS Annika, a cross-linking search engine which can accurately identify
cross-linked peptides in MS2 spectra from a variety of different MS-cleavable
cross-linkers. In this publication, we present MS Annika 2.0, an updated
version implementing a new search algorithm that, in addition to MS2
level, only supports the processing of data from MS2–MS3-based
approaches for the identification of peptides from MS3 spectra, and
introduces a novel scoring function for peptides identified across
multiple MS stages. Detected cross-links are validated by estimating
the false discovery rate (FDR) using a target-decoy approach. We evaluated
the MS3-search-capabilities of MS Annika 2.0 on five different datasets
covering a variety of experimental approaches and compared it to XlinkX
and MaXLinker, two other cross-linking search engines. We show that
MS Annika detects up to 4 times more true unique cross-links while
simultaneously yielding less false positive hits and therefore a more
accurate FDR estimation than the other two search engines. All mass
spectrometry proteomics data along with result files have been deposited
to the ProteomeXchange consortium via the PRIDE partner repository
with the dataset identifier PXD041955.