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

Overview of attention for article published in BMC Bioinformatics, November 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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2 X users
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2 Wikipedia pages

Citations

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88 Dimensions

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Title
PyTMs: a useful PyMOL plugin for modeling common post-translational modifications
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/s12859-014-0370-6
Pubmed ID
Authors

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

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 AbstractPyTMs is a useful PyMOL plugin for modeling common post-translational modifications.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United Kingdom 1 1%
Unknown 93 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 21%
Researcher 18 19%
Student > Master 18 19%
Student > Bachelor 10 10%
Student > Postgraduate 5 5%
Other 12 13%
Unknown 13 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 28%
Agricultural and Biological Sciences 27 28%
Computer Science 7 7%
Chemistry 5 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Other 10 10%
Unknown 17 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 April 2023.
All research outputs
#7,231,506
of 23,578,918 outputs
Outputs from BMC Bioinformatics
#2,751
of 7,398 outputs
Outputs of similar age
#99,366
of 365,227 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 135 outputs
Altmetric has tracked 23,578,918 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,398 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 365,227 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.