Exhaustively Identifying Cross-Linked Peptides with a Linear Computational Complexity

Abstract

Chemical cross-linking coupled to mass spectrometry is a powerful tool to study protein-protein interactions and protein conformations. Two linked peptides are ionized and fragmented to produce a tandem mass spectrum. In such an experiment, a tandem mass spectrum contains ions from two peptides. The peptide identification problem becomes a peptide-peptide pair identification problem. Currently, most tools do not search all possible pairs due to the quadratic time complexity. Consequently, missed findings are unavoidable. In our previous work, we developed a tool named ECL to search all pairs of peptides exhaustively. Unfortunately, it is very slow due to the quadratic computational complexity, especially when the database is large. Furthermore, ECL uses a score function without statistical calibration, while researchers1-3 have proposed that it is inappropriate to directly compare uncalibrated scores because different spectra have different random score distributions. Here we propose an advanced version of ECL, named ECL2. It achieves a linear time and space complexity by taking advantage of the additive property of a score function. It can search a data set containing tens of thousands of spectra against a database containing thousands of proteins in a few hours. Comparison with other five state-of-the-art tools shows that ECL2 is much faster than pLink, StavroX, ProteinProspector, and ECL. Kojak is the only one that is faster than ECL2, but Kojak does not exhaustively search all possible peptide pairs. The comparison shows that ECL2 has the highest sensitivity among the state-of-the-art tools. The experiment using a large-scale in vivo cross-linking data set demonstrates that ECL2 is the only tool that can find the peptide-spectrum matches (PSMs) passing the false discovery rate/q-value threshold. The result illustrates that the exhaustive search and a well-calibrated score function are useful to find PSMs from a huge search space.

The Vaccinia virion: Filling the gap between atomic and ultrastructure

“…Instrument raw files were converted to mgf, mzXML or mzML using MSConvert by ProteoWizard. Using the resulting data, XL were identified using the following XL search engines: Protein Prospector [126], pLINK [127], xQuest (in combination with xProphet) [122], Kojak [128] (in combination with ‘Percolator’ [129–131]), ECL [132] and ECL2 [133], as follows:…”

The labile interactions of cyclic electron flow effector proteins

“…Because ionization efficiency of cross-linked peptides is often low, thus interfering with cross-link identification, the DSP-labeling approach gave us a first hint which Lys residues were easily reacting with the chemical. Ultimately, we attempted to identify the cross-linked residues with the ECL2 algorithm (40). Therefore, as outlined under "Experimental procedures," we used the noncleavable homobifunctional disuccinimidyl suberate (DSS) and thylakoid samples, which were solubilized with ␣DM.…”

“…Similar to experiments with DSP, the Lys cross-linker DSS was added before solubilization. The ECL algorithm (40) deconvoluted the measured LC-MS/MS spectra of Cyt-b 6 fcontaining SDG fractions and identified cross-linked peptides from PETO and su-IV. We were not able to detect DSS-crosslinked peptides from PETO and Cyt-f.…”

Xolik: finding cross-linked peptides with maximum paired scores in linear time

“…Various computational tools have been developed to analyze cross-linking MS data. These tools include MS2Assign (Schilling et al, 2003), xQuest/xProphet (Rinner et al, 2008;Walzthoeni et al, 2012), crux (McIlwain et al, 2010), xComb (Panchaud et al, 2010), Xlink-Identifier (Du et al, 2011), Protein Prospector (Chu et al, 2010;Trnka et al, 2014), pLink (Yang et al, 2012), MeroX (Götze et al, 2014), MXDB (Wang et al, 2014), Kojak (Hoopmann et al, 2015), XlinkX (Liu et al, 2015) and ECL (Yu et al, 2016(Yu et al, , 2017.…”

“…However, their method suffers from the following issues. The time complexity of enumerating bins in ECL2 is O( M ϵ 1 w 2 ), where M is the total range of peptide mass, ϵ 1 is the MS1 tolerance and w is the MS1 bin width (Yu et al, 2017). This time complexity can be rewritten as…”