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Efficient and automated large-scale detection of structural relationships in proteins with a flexible aligner

Overview of attention for article published in BMC Bioinformatics, January 2016
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Title
Efficient and automated large-scale detection of structural relationships in proteins with a flexible aligner
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0866-8
Pubmed ID
Authors

Fernando I. Gutiérrez, Felipe Rodriguez-Valenzuela, Ignacio L. Ibarra, Damien P. Devos, Francisco Melo

Abstract

The total number of known three-dimensional protein structures is rapidly increasing. Consequently, the need for fast structural search against complete databases without a significant loss of accuracy is increasingly demanding. Recently, TopSearch, an ultra-fast method for finding rigid structural relationships between a query structure and the complete Protein Data Bank (PDB), at the multi-chain level, has been released. However, comparable accurate flexible structural aligners to perform efficient whole database searches of multi-domain proteins are not yet available. The availability of such a tool is critical for a sustainable boosting of biological discovery. Here we report on the development of a new method for the fast and flexible comparison of protein structure chains. The method relies on the calculation of 2D matrices containing a description of the three-dimensional arrangement of secondary structure elements (angles and distances). The comparison involves the matching of an ensemble of substructures through a nested-two-steps dynamic programming algorithm. The unique features of this new approach are the integration and trade-off balancing of the following: 1) speed, 2) accuracy and 3) global and semiglobal flexible structure alignment by integration of local substructure matching. The comparison, and matching with competitive accuracy, of one medium sized (250-aa) query structure against the complete PDB database (216,322 protein chains) takes about 8 min using an average desktop computer. The method is at least 2-3 orders of magnitude faster than other tested tools with similar accuracy. We validate the performance of the method for fold and superfamily assignment in a large benchmark set of protein structures. We finally provide a series of examples to illustrate the usefulness of this method and its application in biological discovery. The method is able to detect partial structure matching, rigid body shifts, conformational changes and tolerates substantial structural variation arising from insertions, deletions and sequence divergence, as well as structural convergence of unrelated proteins.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 21%
Student > Ph. D. Student 5 21%
Student > Bachelor 3 13%
Librarian 2 8%
Professor 2 8%
Other 6 25%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 38%
Computer Science 6 25%
Pharmacology, Toxicology and Pharmaceutical Science 3 13%
Biochemistry, Genetics and Molecular Biology 2 8%
Linguistics 1 4%
Other 2 8%
Unknown 1 4%
Attention Score in Context

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 06 January 2016.
All research outputs
#14,554,120
of 23,308,124 outputs
Outputs from BMC Bioinformatics
#4,827
of 7,382 outputs
Outputs of similar age
#208,528
of 395,820 outputs
Outputs of similar age from BMC Bioinformatics
#91
of 141 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,382 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 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.