Detecting cross-linked peptides by searching against a database of cross-linked peptide pairs.

Mass spectrometric identification of cross-linked peptides can provide valuable information about the structure of protein complexes. We describe a straightforward database search scheme that identifies and assigns statistical confidence estimates to spectra from cross-linked peptides. The method is well suited to targeted analysis of a single protein complex, without requiring an isotope labeling strategy. Our approach uses a SEQUEST-style search procedure in which the database is comprised of a mixture of single peptides with and without linkers attached and cross-linked products. In contrast to several previous approaches, we generate theoretical spectra that account for all of the expected peaks from a cross-linked product, and we employ an empirical curve-fitting procedure to estimate statistical confidence measures. We show that our fully automated procedure successfully reidentifies spectra from a previous study, and we provide evidence that our statistical confidence estimates are accurate.