PT  - JOURNAL ARTICLE
AU  - Fengchao Yu
AU  - Ning Li
AU  - Weichuan Yu
TI  - Exhaustively Identifying Cross-Linked Peptides with a Linear Computational Complexity
AID  - 10.1101/097089
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 097089
4099  - http://biorxiv.org/content/early/2017/02/27/097089.short
4100  - http://biorxiv.org/content/early/2017/02/27/097089.full
AB  - Chemical cross-linking coupled with 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 existing tools don’t search all possible pairs due to the quadratic time complexity. Consequently, a significant percentage of linked peptides are missed. In our earlier work, we developed a tool named ECL to search all pairs of peptides exhaustively. While ECL does not miss any linked peptides, 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 researchers1,2 have demonstrated that using a statistical calibrated score function can achieve a higher sensitivity than using an uncalibrated one.Here, we propose an advanced version of ECL, named ECL 2.0. It achieves a linear time and space complexity by taking advantage of the additive property of a score function. It can analyze a typical data set containing tens of thousands of spectra using a large-scale database containing thousands of proteins in a few hours. Comparison with other five state-of-the-art tools shows that ECL 2.0 is much faster than pLink, StavroX, ProteinProspector, and ECL. Kojak is the only one tool that is faster than ECL 2.0. But Kojak does not exhaustively search all possible peptide pairs. We also adopt an e-value estimation method to calibrate the original score. Comparison shows that ECL 2.0 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 ECL 2.0 is the only tool that can find PSMs passing the false discovery rate threshold. The result illustrates that exhaustive search and well calibrated score function are useful to find PSMs from a huge search space.ECLexhaustive cross-linked peptides identification.XL-MSchemical cross-linking coupled with mass spectrometry.ppmparts per million.CIDcollision-induced dissociation.MS1first level mass spectrometry.MS2tandem mass spectrometry.MS3third level mass spectrometry following MS2.DSSdisuccinimidyl suberate.BS3bis(sulfosuccinimidyl) suberate.KSDPkernel spectral dot product.FDRfalse discovery rate.PSMpeptide-spectrum match.