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A probabilistic approach for validating protein NMR chemical shift assignments

Overview of attention for article published in Journal of Biomolecular NMR, May 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#33 of 619)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
wikipedia
4 Wikipedia pages

Citations

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33 Dimensions

Readers on

mendeley
75 Mendeley
citeulike
4 CiteULike
Title
A probabilistic approach for validating protein NMR chemical shift assignments
Published in
Journal of Biomolecular NMR, May 2010
DOI 10.1007/s10858-010-9407-y
Pubmed ID
Authors

Bowei Wang, Yunjun Wang, David S. Wishart

Abstract

It has been estimated that more than 20% of the proteins in the BMRB are improperly referenced and that about 1% of all chemical shift assignments are mis-assigned. These statistics also reflect the likelihood that any newly assigned protein will have shift assignment or shift referencing errors. The relatively high frequency of these errors continues to be a concern for the biomolecular NMR community. While several programs do exist to detect and/or correct chemical shift mis-referencing or chemical shift mis-assignments, most can only do one, or the other. The one program (SHIFTCOR) that is capable of handling both chemical shift mis-referencing and mis-assignments, requires the 3D structure coordinates of the target protein. Given that chemical shift mis-assignments and chemical shift re-referencing issues should ideally be addressed prior to 3D structure determination, there is a clear need to develop a structure-independent approach. Here, we present a new structure-independent protocol, which is based on using residue-specific and secondary structure-specific chemical shift distributions calculated over small (3-6 residue) fragments to identify mis-assigned resonances. The method is also able to identify and re-reference mis-referenced chemical shift assignments. Comparisons against existing re-referencing or mis-assignment detection programs show that the method is as good or superior to existing approaches. The protocol described here has been implemented into a freely available Java program called "Probabilistic Approach for protein Nmr Assignment Validation (PANAV)" and as a web server ( http://redpoll.pharmacy.ualberta.ca/PANAV ) which can be used to validate and/or correct as well as re-reference assigned protein chemical shifts.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 1%
Brazil 1 1%
New Zealand 1 1%
India 1 1%
Russia 1 1%
Belgium 1 1%
Unknown 67 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 32%
Researcher 20 27%
Professor > Associate Professor 5 7%
Professor 4 5%
Student > Master 4 5%
Other 8 11%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 28%
Biochemistry, Genetics and Molecular Biology 17 23%
Chemistry 16 21%
Computer Science 7 9%
Mathematics 1 1%
Other 3 4%
Unknown 10 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 08 April 2022.
All research outputs
#3,178,781
of 23,506,079 outputs
Outputs from Journal of Biomolecular NMR
#33
of 619 outputs
Outputs of similar age
#12,585
of 96,668 outputs
Outputs of similar age from Journal of Biomolecular NMR
#1
of 5 outputs
Altmetric has tracked 23,506,079 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 619 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 94% 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 96,668 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them