Evidence map›Paper›PMID 25431162›Full record

ArticleBMC bioinformatics2014

PyTMs: a useful PyMOL plugin for modeling common post-translational modifications.

Andreas Warnecke, Tatyana Sandalova, Adnane Achour, Robert A Harris

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

61 citing papers in PubMed, 126 citations in OpenAlex.

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  13. Exploring protein functions from structural flexibility using CABS-flex modeling.Protein science : a publication of the Protein Society · 2024
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  19. The fitness cost of spurious phosphorylation.bioRxiv : the preprint server for biology · 2023
    Article
  20. Article

1 more citing papers are in PubMed but not listed here.

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

4 authors at 2 institutions in 1 country.

Andreas WarneckeDepartment of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Applied Immunology & Immunotherapy, L8:04, Karolinska Hospital, SE-171 76, Stockholm, Sweden. andreas.warnecke@ki.se.
Tatyana SandalovaDepartment of Medicine Solna, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. Tatyana.Sandalova@ki.se.
Adnane AchourDepartment of Medicine Solna, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. Adnane.Achour@ki.se.
Robert A HarrisDepartment of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Applied Immunology & Immunotherapy, L8:04, Karolinska Hospital, SE-171 76, Stockholm, Sweden. robert.harris@ki.se.
Karolinska Institutet · SEScience for Life Laboratory · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPost-translational modifications (PTMs) constitute a major aspect of protein biology, particularly signaling events. Conversely, several different pathophysiological PTMs are hallmarks of oxidative imbalance or inflammatory states and are strongly associated with pathogenesis of autoimmune diseases or cancers. Accordingly, it is of interest to assess both the biological and structural effects of modification. For the latter, computer-based modeling offers an attractive option. We thus identified the need for easily applicable modeling options for PTMs.

resultsWe developed PyTMs, a plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common PTMs into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation. Furthermore, advanced settings integrate the pre-selection of surface-exposed atoms, define stereochemical alternatives and allow for basic structure optimization of the newly modified residues.

conclusionPyTMs is a useful, user-friendly modelling plugin for PyMOL. Advantages of PyTMs include standardized generation of PTMs, rapid time-to-result and facilitated user control. Although modeling cannot substitute for conventional structure determination it constitutes a convenient tool that allows uncomplicated exploration of potential implications prior to experimental investments and basic explanation of experimental data. PyTMs is freely available as part of the PyMOL script repository project on GitHub and will further evolve. Graphical Abstract PyTMs is a useful PyMOL plugin for modeling common post-translational modifications.

Indexed as

Protein Processing, Post-TranslationalSoftwareAnimalsCattleEther-A-Go-Go Potassium ChannelsHumansPeptide FragmentsProtein ConformationProteinsSerum Albumin, BovineUser-Computer InterfaceEther-A-Go-Go Potassium ChannelsKCNH1 protein, humanPeptide FragmentsProteinsSerum Albumin, Bovine

Identifiers

PMID25431162
PMCPMC4256751
OpenAlexW2074418202

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.