High Confidence Identification of Cross-Linked Peptides by an Enrichment-Based Dual Cleavable Cross-Linking Technology and Data Analysis tool Cleave-XL
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https://figshare.com/articles/dataset/High_Confidence_Identification_of_Cross-Linked_Peptides_by_an_Enrichment-Based_Dual_Cleavable_Cross-Linking_Technology_and_Data_Analysis_tool_Cleave-XL/11494290
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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.
创建时间:
2020-01-02



