Exhaustive Cross-Linking Search with Protein Feedback

Zhou, Chen; Dai, Shuaijian; Lin, Yuanqiao; Lian, Sheng; Fan, Xiaodan; Li, Ning; Yu, Weichuan

doi:10.1021/acs.jproteome.2c00500

Cited by 10 publications

(16 citation statements)

References 46 publications

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“…The experiments utilized the cleavable cross-linker disuccinimidyl sulfoxide (DSSO) and cyanurbiotindipropionylsuccinimide (CBDPS), and the non-cleavable cross-linker bissulfosuccinimidyl suberate (BS3), and bissulfosuccinimidyl glutarate (BS2G). And we used SeaPIC in conjunction with two different XL-MS search engines, namely pLink2 15 and ECL3 25,26 to run out the final PTM-containing CSMs.…”

Section: Resultsmentioning

confidence: 99%

Searching Post-translational Modifications in Cross-linking Mass Spectrometry Data

Zhou,

Lai,

Dai

et al. 2024

Preprint

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Cross-linking mass spectrometry (XL-MS) is a technique for investigating protein-protein interactions (PPIs) and protein structures. In the realm of biology, post-translational modifications (PTMs) play a critical role in regulating PPIs and reshaping protein structures. However, the identification of PTMs in XL-MS data poses a great computational challenge and thus remains unexplored. In this study, we introduce SeaPIC, the first XL-MS tool that enables biologists to investigate PTMs in PPIs and protein structures. Our experiments demonstrate the successful identification of PTMs within cross-linked peptides, which were previously undiscovered.

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Section: Resultsmentioning

confidence: 99%

Searching Post-translational Modifications in Cross-linking Mass Spectrometry Data

Zhou,

Lai,

Dai

et al. 2024

Preprint

Self Cite

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“…The approach helps constrain the search space (and thus accelerate the search) and it also limits the generation of false positive identifications. Other methods use some elements of compositional analysis to restrict the peptide search space. , CRIMP’s PPI scoring method incorporates all internal evidence for the existence of a protein in the score but does not demand it (Supporting Information). That is, the detection of other crosslinks and all other possible reaction products for a given PPI are incorporated into the score.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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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.

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“…Generally, using information about individual proteins for the assessment of protein pairs, both derived from within the search and derived from external sources, has to be done with great care. Search engines or post-processing tools such as ECL-PF 20 and CRIMP 2.0 21 utilise protein self-crosslinks to assign scores or confidence values to protein heteromeric matches. XLinkProphet 22 uses information about individual proteins being present to rescore matches.…”

Section: Discussionmentioning

confidence: 99%

Rescuing error control in crosslinking mass spectrometry

Fischer,

Rappsilber

2023

Preprint

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Crosslinking mass spectrometry is a powerful tool for studying protein-protein interactions under native or near native conditions in complex mixtures. By help of novel search controls, we show that measures that aim to improve the number of identifications based on heuristic considerations can undermine error estimation in non-obvious ways. The relationship between decoys and false positives is very sensitive to the injection of information that favour likely correct matches. We identify a wider challenge in crosslinking data analysis tools with maintaining the decoy-false positive relationship, and exemplify this with the filtering of results based on the information of which proteins can be observed as having reacted with the crosslinker. Without correcting for this problem, we could identify an average of 260 interspecies protein-protein interactions in 16 analyses, “solidly” suggesting groundbreaking biological connections that don’t actually exist. We also show how data analysis procedures can be tested and modified to rescue the decoy-false positive relationship. The importance of this relationship for reliably identifying protein-protein interactions cannot be overstated.

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Exhaustive Cross-Linking Search with Protein Feedback

Cited by 10 publications

References 46 publications

Searching Post-translational Modifications in Cross-linking Mass Spectrometry Data

Searching Post-translational Modifications in Cross-linking Mass Spectrometry Data

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

Rescuing error control in crosslinking mass spectrometry

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