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High Confidence Identification of Cross-Linked Peptides by an Enrichment-Based Dual Cleavable Cross-Linking Technology and Data Analysis tool Cleave-XL.

Jayanta K ChakrabartySandhya C SadanandaApeksha BhatAishwarya J NaikDhanashri V OstwalSaiful M Chowdhury
Published in: Journal of the American Society for Mass Spectrometry (2020)
Cleavable cross-linking technology requires further MS/MS of the cleavable fragments for unambiguous identification of cross-linked peptides. These spectra are sometimes very ambiguous due to the sensitivity and complex fragmentation pattern of the peptides with the cross-linked residues. We recently reported a dual cleavable cross-linking technology (DUCCT), which can enhance the confidence in the identification of cross-linked peptides. The heart of this strategy is a novel dual mass spectrometry cleavable cross linker that can be cleaved preferentially by two differential tandem mass spectrometry methods, collision induced dissociation and electron transfer dissociation (CID and ETD). Different signature ions from two different mass spectra for the same cross-linked peptide helped identify the cross-linked peptides with high confidence. In this study, we developed an enrichment-based photocleavable DUCCT (PC-DUCCT-biotin), where cross-linked products were enriched from biological samples using affinity purification, and subsequently, two sequential tandem (CID and ETD) mass spectrometry processes were utilized. Furthermore, we developed a prototype software called Cleave-XL to analyze cross-linked products generated by DUCCT. Photocleavable DUCCT was demonstrated in standard peptides and proteins. Efficiency of the software tools to search and compare CID and ETD data of photocleavable DUCCT biotin in standard peptides and proteins as well as regular DUCCT in protein complexes from immune cells were tested. The software is efficient in pinpointing cross-linked sites using CID and ETD cross-linking data. We believe this new DUCCT and associated software tool Cleave-XL will advance high confidence identification of protein cross-linking sites and automated identification of low-resolution protein structures.
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