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Decomposing the space of protein quaternary structures with the interface fragment pair library

Overview of attention for article published in BMC Bioinformatics, January 2015
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Citations

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Title
Decomposing the space of protein quaternary structures with the interface fragment pair library
Published in
BMC Bioinformatics, January 2015
DOI 10.1186/s12859-014-0437-4
Pubmed ID
Authors

Zhong-Ru Xie, Jiawen Chen, Yilin Zhao, Yinghao Wu

Abstract

BackgroundThe physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation.ResultsHere we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library.ConclusionsOur study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 7%
Canada 1 7%
Unknown 13 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Professor > Associate Professor 2 13%
Researcher 2 13%
Student > Master 2 13%
Professor 1 7%
Other 0 0%
Unknown 3 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 40%
Agricultural and Biological Sciences 4 27%
Business, Management and Accounting 1 7%
Computer Science 1 7%
Chemistry 1 7%
Other 0 0%
Unknown 2 13%

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 17 January 2015.
All research outputs
#9,153,215
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#3,757
of 5,420 outputs
Outputs of similar age
#144,289
of 281,086 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 42 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.