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AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules

Overview of attention for article published in Journal of Biomolecular NMR, August 2015
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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 (#47 of 571)
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user

Citations

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56 Dimensions

Readers on

mendeley
66 Mendeley
Title
AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules
Published in
Journal of Biomolecular NMR, August 2015
DOI 10.1007/s10858-015-9970-3
Pubmed ID
Authors

Jason Swails, Tong Zhu, Xiao He, David A. Case

Abstract

We evaluate the performance of the automated fragmentation quantum mechanics/molecular mechanics approach (AF-QM/MM) on the calculation of protein and nucleic acid NMR chemical shifts. The AF-QM/MM approach models solvent effects implicitly through a set of surface charges computed using the Poisson-Boltzmann equation, and it can also be combined with an explicit solvent model through the placement of water molecules in the first solvation shell around the solute; the latter substantially improves the accuracy of chemical shift prediction of protons involved in hydrogen bonding with solvent. We also compare the performance of AF-QM/MM on proteins and nucleic acids with two leading empirical chemical shift prediction programs SHIFTS and SHIFTX2. Although the empirical programs outperform AF-QM/MM in predicting chemical shifts, the differences are in some cases small, and the latter can be applied to chemical shifts on biomolecules which are outside the training set employed by the empirical programs, such as structures containing ligands, metal centers, and non-standard residues. The AF-QM/MM described here is implemented in version 5 of the SHIFTS software, and is fully automated, so that only a structure in PDB format is required as input.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Belgium 1 2%
Unknown 64 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 24%
Student > Ph. D. Student 15 23%
Professor 6 9%
Student > Master 6 9%
Student > Bachelor 4 6%
Other 6 9%
Unknown 13 20%
Readers by discipline Count As %
Chemistry 30 45%
Biochemistry, Genetics and Molecular Biology 14 21%
Agricultural and Biological Sciences 3 5%
Medicine and Dentistry 2 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 6%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 29 August 2015.
All research outputs
#4,000,915
of 24,903,209 outputs
Outputs from Journal of Biomolecular NMR
#47
of 571 outputs
Outputs of similar age
#47,653
of 269,970 outputs
Outputs of similar age from Journal of Biomolecular NMR
#1
of 11 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 571 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 91% 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 269,970 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 82% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.