Evidence map›Paper›PMID 35696073›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2022

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.

Anthony A Iannetta, Leslie M Hicks

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Anthony A IannettaDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Leslie M HicksDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. lmhicks@unc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Indexed as

Computational BiologyProtein Processing, Post-TranslationalComputer SimulationMass SpectrometryProteomicsBioinformaticsBottom-up proteomicsDatabase searchingLiquid chromatography–tandem mass spectrometryPost-translational modificationsPTM enrichment

Identifiers

PMID35696073

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.