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Sequence specificity between interacting and non-interacting homologs identifies interface residues – a homodimer and monomer use case

Overview of attention for article published in BMC Bioinformatics, October 2015
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Title
Sequence specificity between interacting and non-interacting homologs identifies interface residues – a homodimer and monomer use case
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0758-y
Pubmed ID
Authors

Qingzhen Hou, Bas E. Dutilh, Martijn A. Huynen, Jaap Heringa, K. Anton Feenstra

Abstract

Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites. We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9 % recall and up to 25.1 % precision. To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Germany 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 28%
Student > Bachelor 3 12%
Student > Ph. D. Student 3 12%
Student > Master 3 12%
Professor 1 4%
Other 1 4%
Unknown 7 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 24%
Agricultural and Biological Sciences 6 24%
Computer Science 2 8%
Economics, Econometrics and Finance 2 8%
Physics and Astronomy 1 4%
Other 0 0%
Unknown 8 32%
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 13 October 2015.
All research outputs
#15,348,067
of 22,829,683 outputs
Outputs from BMC Bioinformatics
#5,375
of 7,287 outputs
Outputs of similar age
#163,000
of 278,190 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 139 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.