Evidence map›Paper›PMID 37552582›Full record

ArticleJournal of proteome research2023

Integrated Analysis of Cross-Links and Dead-End Peptides for Enhanced Interpretation of Quantitative XL-MS.

Andrew Keller, Xiaoting Tang, James E Bruce

Abstract read
In one paragraph

Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Andrew KellerDepartment of Genome Sciences, University of Washington, Seattle, Washington 98105 ,United States.
Xiaoting TangDepartment of Genome Sciences, University of Washington, Seattle, Washington 98105 ,United States.
James E BruceDepartment of Genome Sciences, University of Washington, Seattle, Washington 98105 ,United States.ORCID 0000-0001-6441-6089

Funding

Dynamics of the cellular interactomeR35GM136255 · NIGMS · UNIVERSITY OF WASHINGTON · PI James Edward Bruce · 2020 to 2026
$4.0M
The heart failure interactomeR01HL144778 · NHLBI · UNIVERSITY OF WASHINGTON · PI BRUCE, JAMES EDWARD · 2019 to 2022
$2.7M
Aging Mitochondrial InteractomeR01AG078279 · NIA · UNIVERSITY OF WASHINGTON · PI James Edward Bruce, David J. Marcinek · 2023 to 2026
$2.2M
Structure-function changes in the mitochondrial interactome with ageR56AG070096 · NIA · UNIVERSITY OF WASHINGTON · PI BRUCE, JAMES EDWARD, MARCINEK, DAVID J. · 2021 to 2021
$362k
Aging Mitochondrial InteractomeR56AG078279 · NIA · UNIVERSITY OF WASHINGTON · PI BRUCE, JAMES EDWARD, MARCINEK, DAVID J. · 2022 to 2022
$361k
NHLBI NIH HHS R01 HL144778NIA NIH HHS R01 AG078279NIA NIH HHS R56 AG070096NIA NIH HHS R56 AG078279NIGMS NIH HHS R35 GM136255
6 · The paper itself

Abstract

Chemical cross-linking with mass spectrometry provides low-resolution structural information on proteins in cells and tissues. Combined with quantitation, it can identify changes in the interactome between samples, for example, control and drug-treated cells or young and old mice. A difference can originate from protein conformational changes that alter the solvent-accessible distance separating the cross-linked residues. Alternatively, a difference can result from conformational changes localized to the cross-linked residues, for example, altering the solvent exposure or reactivity of those residues or post-translational modifications of the cross-linked peptides. In this manner, cross-linking is sensitive to a variety of protein conformational features. Dead-end peptides are cross-links attached only at one end to a protein with the other terminus being hydrolyzed. As a result, changes in their abundance reflect only conformational changes localized to the attached residue. For this reason, analyzing both quantified cross-links and their corresponding dead-end peptides can help elucidate the likely conformational changes giving rise to observed differences in cross-link abundance. We describe analysis of dead-end peptides in the XLinkDB public cross-link database and, with quantified mitochondrial data isolated from failing heart versus healthy mice, show how a comparison of abundance ratios between cross-links and their corresponding dead-end peptides can be leveraged to reveal possible conformational explanations.

Indexed as

PeptidesProteinsAnimalsCross-Linking ReagentsMass SpectrometryMiceProtein ConformationSolventsCross-Linking ReagentsPeptidesProteinsSolventschemical cross-linkingcross-link databasedead-end peptidesmass spectrometryprotein complexesprotein structuresquantitative XL-MS

Identifiers

PMID37552582
PMCPMC10866149

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.