Evidence map›Paper›PMID 38873895›Full record

ReviewJournal of mass spectrometry : JMS2024

The path forward for protein footprinting, covalent labeling, and mass spectrometry-based protein conformational analyses.

Nicholas B Borotto

Abstract readReview
In one paragraph

Review in Journal of mass spectrometry : JMS, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nicholas B BorottoDepartment of Chemistry, University of Nevada, Reno, Nevada, USA.ORCID https://orcid.org/0000-0003-2793-1481

Funding

Rapid Assessment of Proteoform-Resolved Higher-Order StructuresR35GM151104 · NIGMS · UNIVERSITY OF NEVADA RENO · PI Nicholas Brent Borotto · 2023 to 2026
$1.4M
NIGMS NIH HHS R35 GM151104NIH HHS 1R35GM151104-01
6 · The paper itself

Abstract

Mass spectrometry-based approaches to assess protein conformation have become widely utilized due to their sensitivity, low sample requirements, and broad applicability to proteins regardless of size and environment. Their wide applicability and sensitivity also make these techniques suitable for the analysis of complex mixtures of proteins, and thus, they have been applied at the cell and even the simple organism levels. These works are impressive, but they predominately employ "bottom-up" workflows and require proteolytic digestion prior to analysis. Once digested, it is not possible to distinguish the proteoform from which any single peptide is derived and therefore, one cannot associate distal-in primary structure-concurrent post-translational modifications (PTMs) or covalent labels, as they would be found on separate peptides. Thus, analyses via bottom-up proteomics report the average PTM status and higher-order structure (HOS) of all existing proteoforms. Second, these works predominately employ promiscuous reagents to probe protein HOS. While this does lead to improved conformational resolution, the formation of many products can divide the signal associated with low-copy number proteins below signal-to-noise thresholds and complicate the bioinformatic analysis of these already challenging systems. In this perspective, I further detail these limitations and discuss the positives and negatives of top-down proteomics as an alternative.

Indexed as

Mass SpectrometryProtein ConformationProtein FootprintingProtein Processing, Post-TranslationalProteinsProteomicsProteinsanalytical chemistryhigher order structuremass spectrometryproteinsreview

Identifiers

PMID38873895
PMCPMC11210343

What OpenQuestion holds

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Registered trials

None linked

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.