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PCalign: a method to quantify physicochemical similarity of protein-protein interfaces

Overview of attention for article published in BMC Bioinformatics, February 2015
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Title
PCalign: a method to quantify physicochemical similarity of protein-protein interfaces
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0471-x
Pubmed ID
Authors

Shanshan Cheng, Yang Zhang, Charles L Brooks

Abstract

BackgroundStructural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques.ResultsWe developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity.ConclusionsPCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 6%
United States 2 6%
Germany 1 3%
Unknown 29 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 9 26%
Student > Bachelor 4 12%
Professor 3 9%
Other 3 9%
Other 2 6%
Unknown 3 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 32%
Agricultural and Biological Sciences 11 32%
Computer Science 3 9%
Chemistry 2 6%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 5 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 February 2015.
All research outputs
#18,616,159
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#6,100
of 7,454 outputs
Outputs of similar age
#248,717
of 357,526 outputs
Outputs of similar age from BMC Bioinformatics
#111
of 135 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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