High-Sensitivity Proteome-Scale Searches for Crosslinked Peptides Using CRIMP 2.0

Abstract

Crosslinking mass spectrometry (XL-MS) is a valuable technique for generating point-to-point distance measurements in protein space. However, cell-based XL-MS experiments require efficient software that can detect crosslinked peptides with sensitivity and controlled error rates. Many algorithms implement a filtering strategy designed to reduce the size of the database prior to mounting a search for crosslinks, but concern has been expressed over the possibility of reduced sensitivity using these strategies. We present a new scoring method that uses a rapid presearch method and a concept inspired by computer vision algorithms to resolve crosslinks from other conflicting reaction products. Searches of several curated crosslink datasets demonstrate high crosslink detection rates, and even the most complex proteome-level searches (using cleavable or noncleavable crosslinkers) can be completed efficiently on a conventional desktop computer. The detection of protein-protein interactions is increased twofold through the inclusion of compositional terms in the scoring equation. The combined functionality is made available as CRIMP 2.0 in the Mass Spec Studio.

Figure 1.

Schematic outlining the typical approaches to searching MS² datasets for evidence of peptide crosslinking. The 1-pass approach begins with the precursor mass of the putative crosslinked peptide and constrains a database search through a simple three-term sum involving the α peptide mass, the β peptide mass and the linker mass. Combinations are then searched against the MS² spectrum. The 2-pass method begins and ends with the MS² spectrum. First, candidate α and β peptides are found in the MS² data, and only then is precursor mass used to constrain combinations for a more exhaustive search of the MS² data.

Figure 2.

Crosslink sensitivity determination using the replicate dataset from Beveridge et al. for the DSS crosslinker. The analysis for the Cas9 database at (A) 5% and (B) 1% calculated FDR. The results for the segmented Cas9 database at (C) 5% and (D) 1% FDR, presented as both intraprotein and an interprotein search results. Effect of entrapment is shown using multiple added databases with the noted protein complexity. Real % FDR posted as callouts.

Figure 3.

Average crosslinked peptide numbers using DSSO as the crosslinking reagent and a stepped HCD MS² method for data acquisition. All results for the noted algorithms are derived from Matzinger et al. with the addition of CRIMP 2.0, at an expected FDR of 1% (left bar in pair), and corrected results are shown using a post-score cutoff to reach an experimentally validated FDR of 1% (right bar in pair). True positives in blue, false positives in orange. Real % FDR posted as callouts. Error bars indicate standard deviation of the total hits, n=3.

Figure 4.

Number of detected PPIs in synthetic peptide benchmark dataset 2, from Matzinger et al. Blue bars represent a search conducted at an estimated 5% FDR and orange bars a search conducted at an estimated 1% FDR. All three sets of data from the benchmark were searched, with the indicated numbers of proteins in the search database to explore the effect of entrapment. Real FDR values are indicated as callouts.

Figure 5.

PPI search results for the in-situ crosslinking of the E. coli proteome using two crosslinking reagents. (A) searches conducted at a targeted 5% FDR and (B) searches conducted with a targeted 1% FDR. Results are based on the approximate PPI database established in Lenz et al., using the composition-informed PPI scoring method. Percentages at the bottom of the figure show calculated FDR values based on the composition of the library.