Dataset Information

Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.

ABSTRACT: Cross-linking mass spectrometry (XL-MS) has become an indispensable tool for the emerging field of systems structural biology over the recent years. However, the confidence in individual protein-protein interactions (PPIs) depends on the correct assessment of individual inter-protein cross-links. In this article, we describe a mono- and intralink filter (mi-filter) that is applicable to any kind of cross-linking data and workflow. It stipulates that only proteins for which at least one monolink or intra-protein cross-link has been identified within a given data set are considered for an inter-protein cross-link and therefore participate in a PPI. We show that this simple and intuitive filter has a dramatic effect on different types of cross-linking data ranging from individual protein complexes over medium-complexity affinity enrichments to proteome-wide cell lysates and significantly reduces the number of false-positive identifications for inter-protein links in all these types of XL-MS data.

SUBMITTER: Chen X

PROVIDER: S-EPMC9798375 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.

Chen Xingyu X Sailer Carolin C Kammer Kai Michael KM Fürsch Julius J Eisele Markus R MR Sakata Eri E Pellarin Riccardo R Stengel Florian F

Analytical chemistry 20221212 51

Cross-linking mass spectrometry (XL-MS) has become an indispensable tool for the emerging field of systems structural biology over the recent years. However, the confidence in individual protein-protein interactions (PPIs) depends on the correct assessment of individual inter-protein cross-links. In this article, we describe a mono- and intralink filter (mi-filter) that is applicable to any kind of cross-linking data and workflow. It stipulates that only proteins for which at least one monolink ...[more]

PMID: 36510358

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Project description:All large scale LC-MS/MS post-translational methylation site discovery experiments require methylpeptide spectrum matches (methyl-PSMs) to be identified at acceptably low false discovery rates (FDRs). To meet estimated methyl-PSM FDRs, methyl-PSM filtering criteria are often determined using the target-decoy approach. The efficacy of this methyl-PSM filtering approach has, however, yet to be thoroughly evaluated. Here, we conduct a systematic analysis of methyl-PSM FDRs across a range of sample preparation workflows (each differing in their exposure to the alcohols methanol and isopropyl alcohol) and mass spectrometric instrument platforms (each employing a different mode of MS/MS dissociation). Through (13)CD3-methionine labeling (heavy-methyl SILAC) of Saccharomyces cerevisiae cells and in-depth manual data inspection, accurate lists of true positive methyl-PSMs were determined, allowing methyl-PSM FDRs to be compared with target-decoy approach-derived methyl-PSM FDR estimates. These results show that global FDR estimates produce extremely unreliable methyl-PSM filtering criteria; we demonstrate that this is an unavoidable consequence of the high number of amino acid combinations capable of producing peptide sequences that are isobaric to methylated peptides of a different sequence. Separate methyl-PSM FDR estimates were also found to be unreliable due to prevalent sources of false positive methyl-PSMs that produce high peptide identity score distributions. Incorrect methylation site localizations, peptides containing cysteinyl-S-β-propionamide, and methylated glutamic or aspartic acid residues can partially, but not wholly, account for these false positive methyl-PSMs. Together, these results indicate that the target-decoy approach is an unreliable means of estimating methyl-PSM FDRs and methyl-PSM filtering criteria. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large scale LC-MS/MS methylation site discovery experiments and make recommendations on how to reduce methyl-PSM FDRs in samples not amenable to heavy isotope labeling. Data are available via ProteomeXchange with the data identifier PXD002857.

Dataset Information

Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.

Publications

Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.

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