Evidence map›Paper›PMID 36350547›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Bioinformatical Approaches to the Discovery and Classification of Novel Deubiquitinases.

Thomas Hermanns, Kay Hofmann

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

2 authors at 1 institution in 1 country.

Thomas HermannsInstitute for Genetics, University of Cologne, Cologne, Germany.
Kay HofmannInstitute for Genetics, University of Cologne, Cologne, Germany. kay.hofmann@uni-koeln.de.
University of Cologne · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deubiquitinating enzymes (DUBs) are active at multiple levels of the eukaryotic ubiquitin system. DUBs are important for ubiquitin activation and maintaining cellular ubiquitin levels but can also edit or dissolve ubiquitin chains or deconjugate ubiquitin from substrates. Eukaryotic DUBs can be grouped into seven molecular classes, most of which enzymes are cysteine proteases assuming the papain fold. In recent years, an ever-increasing number of pathogen-encoded DUBs have been characterized, which are active inside the host cell and help the pathogens to evade the defense response. At first sight, bacterial and viral DUBs appear to be very different from their eukaryotic counterparts, making them hard to identify by bioinformatic methods. However, apart from very few exceptions, bacterial and viral DUBs are distantly related to eukaryotic DUB classes and possess several hallmarks that can be used to identify high-confidence DUB candidates from pathogen genomes - even in the complete absence of biochemical or functional annotation. This chapter addresses bioinformatical DUB discovery approaches based on a previously published analysis of DUB evolution. The core set of bioinformatical tools required for this endeavor are freely accessible and do not require a particular bioinformatics infrastructure.

Indexed as

Deubiquitinating EnzymesUbiquitinPapainUbiquitinationDeubiquitinating EnzymesPapainUbiquitinDeubiquitinating enzymesEvolutionPapain-fold cysteine proteasesUbiquitin

Identifiers

PMID36350547
OpenAlexW4308750856

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.