MXDB

Summary

Chemical cross-linking and mass spectrometry have recently been shown to constitute a powerful tool to study protein-protein interactions and to help elucidate the structure of large protein complexes. However, computational methods to interpret the complex MS/MS spectra from linked peptides are still in their infancy, thus making the high-throughput application of this approach largely impractical. Due to the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to 1) efficiently generate large mass spectral reference data for linked peptides at a low cost and 2) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we can identify thousands of MS/MS spectra from disulfide-bridged peptides against proteome-scale sequence databases and significantly improve the sensitivity of identifying cross-linked peptides. This allows us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be a valuable resource for the future development of new tools for the identification of linked peptide.