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COZOID: contact zone identifier for visual analysis of protein-protein interactions

Overview of attention for article published in BMC Bioinformatics, April 2018
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4 tweeters


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12 Mendeley
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COZOID: contact zone identifier for visual analysis of protein-protein interactions
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2113-6
Pubmed ID

Katarína Furmanová, Jan Byška, Eduard M. Gröller, Ivan Viola, Jan J. Paleček, Barbora Kozlíková


Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge. In this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. It helps proteomic experts to select the most relevant configurations and explore their contact zones at different levels of detail. The system integrates a set of methods that follow and support the workflow of proteomics experts. The first visual abstraction method, the Matrix view, is based on customized interactive heat maps and provides the users with an overview of all possible residue-residue contacts in all PPI configurations and their interactive filtering. In this step, the user can traverse all input PPI configurations and obtain an overview of their interacting amino acids. Then, the models containing a particular pair of interacting amino acids can be selectively picked and traversed. Detailed information on the individual amino acids in the contact zones and their properties is presented in the Contact-Zone list-view. The list-view provides a comparative tool to rank the best models based on the similarity of their contacts to the template-structure contacts. All these techniques are interactively linked with other proposed methods, the Exploded view and the Open-Book view, which represent individual configurations in three-dimensional space. These representations solve the high overlap problem associated with many configurations. Using these views, the structural alignment of the best models can also be visually confirmed. We developed a system for the exploration of large sets of protein-protein complexes in a fast and intuitive way. The usefulness of our system has been tested and verified on several docking structures covering the three major types of PPIs, including coiled-coil, pocket-string, and surface-surface interactions. Our case studies prove that our tool helps to analyse and filter protein-protein complexes in a fraction of the time compared to using previously available techniques.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 33%
Professor > Associate Professor 3 25%
Student > Master 2 17%
Researcher 1 8%
Unknown 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Agricultural and Biological Sciences 3 25%
Computer Science 3 25%
Neuroscience 1 8%
Chemistry 1 8%
Other 0 0%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 July 2021.
All research outputs
of 18,999,792 outputs
Outputs from BMC Bioinformatics
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Outputs of similar age
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Outputs of similar age from BMC Bioinformatics
of 24 outputs
Altmetric has tracked 18,999,792 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,493 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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 291,355 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.