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IPC – Isoelectric Point Calculator

Overview of attention for article published in Biology Direct, October 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
12 tweeters
wikipedia
17 Wikipedia pages

Citations

dimensions_citation
143 Dimensions

Readers on

mendeley
249 Mendeley
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Title
IPC – Isoelectric Point Calculator
Published in
Biology Direct, October 2016
DOI 10.1186/s13062-016-0159-9
Pubmed ID
Authors

Lukasz P. Kozlowski

Abstract

Accurate estimation of the isoelectric point (pI) based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry. Additionally, pI estimation can be helpful during protein crystallization trials. Here, I present the Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including two new computationally optimized pKa sets. According to the presented benchmarks, the newly developed IPC pKa sets outperform previous algorithms by at least 14.9 % for proteins and 0.9 % for peptides (on average, 22.1 % and 59.6 %, respectively), which corresponds to an average error of the pI estimation equal to 0.87 and 0.25 pH units for proteins and peptides, respectively. Moreover, the prediction of pI using the IPC pKa's leads to fewer outliers, i.e., predictions affected by errors greater than a given threshold. The IPC service is freely available at http://isoelectric.ovh.org Peptide and protein datasets used in the study and the precalculated pI for the PDB and some of the most frequently used proteomes are available for large-scale analysis and future development. This article was reviewed by Frank Eisenhaber and Zoltán Gáspári.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Malaysia 1 <1%
France 1 <1%
South Africa 1 <1%
Unknown 244 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 57 23%
Student > Ph. D. Student 56 22%
Student > Master 35 14%
Researcher 24 10%
Student > Doctoral Student 10 4%
Other 34 14%
Unknown 33 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 67 27%
Agricultural and Biological Sciences 44 18%
Chemistry 30 12%
Pharmacology, Toxicology and Pharmaceutical Science 13 5%
Engineering 12 5%
Other 38 15%
Unknown 45 18%

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 12 October 2020.
All research outputs
#2,204,189
of 16,579,356 outputs
Outputs from Biology Direct
#114
of 596 outputs
Outputs of similar age
#55,426
of 297,437 outputs
Outputs of similar age from Biology Direct
#5
of 41 outputs
Altmetric has tracked 16,579,356 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 596 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done well, scoring higher than 80% 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 297,437 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 81% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.