Exhaustive Cross-linking Search with Protein Feedback

Zhou, Chen; Dai, Shuaijian; Lin, Yuanqiao; Li, Ning; Yu, Weichuan

doi:10.1101/2022.03.03.482813

Cited by 2 publications

(5 citation statements)

References 41 publications

(58 reference statements)

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“…The approach helps constrain the search space (and thus accelerate the search) and it also helps to limit the generation of false positive identifications. Other methods use some element of compositional analysis to restrict the peptide search space 45,46 . CRIMP’ s PPI scoring method incorporates all internal evidence for the existence of a given protein in the score but does not demand it (Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0

Crowder

Sarpe

Amaral

et al. 2023

Preprint

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Crosslinking mass spectrometry (XL-MS) is a valuable technique for the generation of point-to-point distance measurements in protein space. Applications involving in situ chemical crosslinking have created the possibility of mapping whole protein interactomes with high spatial resolution. However, an XL-MS experiment carried out directly on cells requires highly efficient software that can detect crosslinked peptides with sensitivity and controlled error rates. Many algorithmic approaches invoke 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 with such strategies. Here we present a full upgrade to CRIMP, the crosslinking app in the Mass Spec Studio, which implements a new strategy for the detection of both component peptides in the MS2 spectrum. Using several published datasets, we demonstrate that this pre-searching method is sensitive and fast, permitting whole proteome searches on a conventional desktop computer for both cleavable and noncleavable crosslinkers. We introduce a new strategy for scoring crosslinks, adapted from computer vision algorithms, that properly resolves conflicting XL hits from other crosslinking reaction products, and we present a method for enhancing the detection of protein-protein interactions that relies upon compositional data.

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Section: Resultsmentioning

confidence: 99%

High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0

Crowder

Sarpe

Amaral

et al. 2023

Preprint

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“…Although different software may prioritize candidates with similar peptide scores differently, the global information on the data set represented by the protein scores can feedback on estimating the significance of the peptide candidates. The effectiveness and proper FDR control when PFM is applied in linear peptide identification have been validated using non-cross-linked peptides . Since PFM is quite new, we added one experiment for further verification, as shown in Supporting Information Note 9.…”

Section: Methodsmentioning

confidence: 99%

“…Since PFM is quite new, we added one experiment for further verification, as shown in Supporting Information Note 9. More details and validation of the PFM can be found in the poster …”

Section: Methodsmentioning

confidence: 99%

“…The effectiveness and proper FDR control when PFM is applied in linear peptide identification have been validated using non-cross-linked peptides. 42 Since PFM is quite new, we added one experiment for further verification, as shown in Supporting Information Note 9. More details and validation of the PFM can be found in the poster.…”

Section: Pfmmentioning

confidence: 99%

“…More details and validation of the PFM can be found in the poster. 42 For series raw files that are fractionated from a single sample, PIPI2 searches them jointly. Peptides from the same sample are distributed into different fractions where the peptides differ significantly while the proteins tend to be quite similar.…”

Section: Pfmmentioning

confidence: 99%

See 2 more Smart Citations

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications

Lai,

Zhao,

Zhou

et al. 2024

J. Proteome Res.

Self Cite

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Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

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Exhaustive Cross-linking Search with Protein Feedback

Cited by 2 publications

References 41 publications

High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0

High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications

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