Cited by 33Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsClick Chemistry and Applications

Transforming Chemical Proteomics Enrichment into a High-Throughput Method Using an SP2E Workflow

Open access
Becker, Tobias, Wiest, Andreas, Telek, András, Bejko, Daniel, Hoffmann-Röder, Anja, Kielkowski, Pavel·2022·JACS Au

Published version of a preprint

Abstract

Protein post-translational modifications (PTMs) play a critical role in the regulation of protein catalytic activity, localization, and protein-protein interactions. Attachment of PTMs onto proteins significantly diversifies their structure and function, resulting in proteoforms. However, the sole identification of post-translationally modified proteins, which are often cell type and disease-specific, is still a highly challenging task. Substoichiometric amounts and modifications of low abundant proteins necessitate the purification or enrichment of the modified proteins. Although the introduction of mass spectrometry-based chemical proteomic strategies has enabled the screening of protein PTMs with increased throughput, sample preparation remains highly time-consuming and tedious. Here, we report an optimized workflow for the enrichment of PTM proteins in a 96-well plate format, which could be extended to robotic automation. This platform allows us to significantly lower the input of total protein, which opens up the opportunity to screen specialized and difficult-to-culture cell lines in a high-throughput manner. The presented SP2E protocol is robust and time- and cost-effective, as well as suitable for large-scale screening of proteoforms. The application of the SP2E protocol will thus enable the characterization of proteoforms in various processes such as neurodevelopment, neurodegeneration, and cancer. This may contribute to an overall acceleration of the recently launched Human Proteoform Project.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/jacsau.2c00284

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Becker, T., Wiest, A., Telek, A., Bejko, D., Hoffmann-Röder, A., & Kielkowski, P.
Affiliations
  • Ludwig-Maximilians-Universität München (DE)
  • Department of Chemistry, LMU Munich, 81377 Munich, Germany
Corresponding author
Kielkowski, Pavel
Venue
JACS Au
Published
2022-06-30
Volume
2
Issue
7
Pages
1712-1723
ISSN
2691-3704 (ISSN-L)
OpenAlex
W4283743000
Cited by
33
References
55
Citations by year
2026: 14 · 2025: 8 · 2024: 7 · 2023: 2 · 2022: 2
FWCI
1.72
Type
journal article
Language
en
Open access
Locations
  • JACS Au (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (SFB1309)
  • Verband der Chemischen Industrie
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Physical Sciences › Chemistry › Organic Chemistry › Click Chemistry and Applications
Keywords
chemoproteomics, post-translational modification, proteoforms, high-throughput sample preparation, 96-well format, robotic automation, low-abundance proteins, neurodegeneration, cancer

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review